Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Rainstorm-induced flood risk assessment in developed urban area using a data-driven approach with watershed units

Rainstorm flooding in developed urban areas has become a global focus. This study proposes a data-driven approach to urban rainstorm flood risk assessment. In contrast to the existing research, this study focuses on terrain watersheds as an assessment unit. Using Changsha as the study area, an inventory of 238 historical rainstorm flood locations was produced using automatic web crawling and literature data mining. Subsequently, an assessment model was developed based on a Bayesian algorithm and 16 influencing factors, and its accuracy was verified using a receiver operating characteristic curve. Because underground infrastructure is prone to backflow at its entrances and exits during rainstorms, the developed model was used to assess the backflow risk of two typical underground structures subjected to three rainstorm return periods: 5 (scenario 1), 10 (scenario 2), and 100 years (scenario 3). The conclusions are as follows: (1) The proposed method has a prediction accuracy of 88 % for flood risk. The most influential factors were H11 (proportion of impervious surface), H4 (mean elevation), and H1 (rainfall), contributing 52 %, 14.3 %, and 11.9 %, respectively. (2) Watersheds are classified into "Very Low," "Low," "High," and "Very High" based on the degree of flooding impact, accounting for 83.6 %, 11.9 %, 3.9 %, and 0.7 %, respectively. Watersheds classified as "Very High" are mainly distributed in the central region. (3) A total of 48 subway stations (7.9 % of the total) and 148 underground parking lots (6.5 % of the total) in the study area are located in "Very High" risk areas. (4) Compared to that in scenario 1, the proportion of underground entrances and exits with a "Very high" protection level in scenario 3 increased by approximately 10 %. In conclusion, this framework can assist urban planners in understanding the risks of urban flooding and mitigating potential flooding impacts.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India)

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Flood susceptibility mapping using Sentinel 1 and frequency ratio technique in Jinjiram River watershed, India

Assam is one of the most flood-prone states in India, and the state frequently experiences catastrophic floods that cause significant damage in terms of loss of life and property. Flood susceptibility is considered the most essential and crucial input for managing floodplains and fostering local and regional development. This study focuses on the generation of flood susceptibility maps using the Frequency Ratio (FR) technique and microwave remote sensing inputs in the Jinjiram watershed which experienced disastrous flooding in 2020. The study has been carried out by taking into consideration of different morphological, lithological, and hydrological factors. In this study the flood inventory map was created by extracting the time series SAR (Synthetic Aperture Radar) Sentinel 1 GRD (Ground Range Detected) images of flooded areas for the past 5 years, from 2016 to 2020. A total of 72 inventory samples were identified of which 70% of total flooded samples were chosen for training and 30% for model testing at random basis. Applying these FR methods, the study determines a range of flood susceptibility which was then divided into five classes, from very low to very high. The Receiver Operating Characteristics (ROC) analysis was used to evaluate the accuracy of flood susceptibility maps generated using FR models. The AUC of ROC in flood susceptibility mapping is 0.81167 achieved, corresponding to a prediction accuracy of 81.17%. The findings can be used to calculate risk, develop flood control measures and infrastructural policies, and formulate sustainable water management policies for the watershed.

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, "antecedent precipitation history" and "seasonal time occurrence of rainfall," significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h.

Influence of respondents' Differentiation of subjective response on water knowledge stock test scale: Evaluation based on two-parameter-multidimensional IRT model

Insufficient awareness of water issues is a crucial bottleneck restricting the sustainable utilization of water resources. To accurately measure citizens' water knowledge stock and overcome the differences between scales and respondents' characteristic levels on test results, the research focuses on developing and evaluating water knowledge stock test scales. The mechanism for identifying indicators is designed based on the grounded theory, and as a result, the water knowledge stock test indicator system is derived. The data was collected by the form of survey questionnaire developed with the test indicator system. A two-parameter multidimensional item response theoretical model is constructed based on item parameter estimation, data model fitting, and item information function. The survey data and optimization model are used to optimize the water knowledge stock test scale and verify the fitting degree with the characteristics of the respondents. The test information function and standard error function indicate that the scale is most informative for individuals with characteristic levels ranging from -2 to 3, resulting in a highly reliable test effect. The research has established a measurement indicator system, methodology, and presented results that serve as a foundation for measuring the stock of water knowledge.

A synergistic approach towards understanding flood risks over coastal multi-hazard environments: Appraisal of bivariate flood risk mapping through flood hazard, and socio-economic-cum-physical vulnerability dimensions

The dynamics of flood risk over Coastal Multi-hazard Catchments (CMC) exhibit bizarre characteristics. In these regions, flood hazards are governed by a complex interaction of multiple flood-inducing sources; varying in magnitudes, origin, and direction of propagation. Our conventional understanding of vulnerability may be obscure within these catchments. This can be attributable to the heterogeneous nature of various physical and socio-economic entities. The study proposes a comprehensive framework to quantify bivariate flood risks over a severely flood-prone region in India. The study considers flood hazards, along with vulnerabilities transpiring from (a) physical, (b) socio-economic, and (c) composite (combination of both) groups of indicators. To overcome data scarcity prevalent in CMCs, CHIRPS v2.0, a high-resolution Satellite Precipitation Product, along with other ancillary datasets, are forced to 1D2D coupled MIKE+ hydrodynamic model to simulate flood hazards. A set of 24 indicators are considered within the Shannon Entropy-cum-TOPSIS framework to derive three types of vulnerability. The marginal and compound contributions of hazard and each vulnerability type are represented through a novel concept of bivariate flood risk classifier at the village scale. We notice high and very-high flood hazards over the coastline and floodplains. An equitable influence of socio-economic vulnerability and hazards is reflected, as they cover 41 % of villages together under varied degrees of flood risks. The impacts of hazards are underscored in the presence of physical vulnerability, as the latter contributes to risks in about 72 % of villages. Composite vulnerability prevails its impact over 53 % of villages, dominating its influence on flood risks over hazards. The study delivers vital information to the global flood management community on the prudent selection of indicators, as their influence is markedly noticed on the overall flood risks. The diversified characteristics of flood risk inspire a rationalized implementation of structural and non-structural options in resource-constrained conditions.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques

The daily soil radon activity has been measured continuously over a year with BARASOL BMC2 probe at a measuring site of Jadavpur University Campus in Kolkata, India. The dependency of soil radon activity with different atmospheric parameters such as soil temperature, soil pressure, humidity, air temperature, and rainfall has been also analyzed. The whole study period is divided in four seasons as proposed by the Indian Meteorological Department (IMD). Minimum soil radon level has been observed during the winter season (December-February). On the other hand, higher soil radon level has been observed both for summer and monsoon. Except soil pressure, all other variables have shown positive correlation with soil radon activity. Among five variables, soil temperature has been the most significant variable in terms of correlation with soil radon level whereas maximum humidity has been the least significant correlated variable. It has been observed that considerable reduction of soil radon level occurred after four heavy rainfall events during the study period. The combined effect of these multi-parameters on soil radon gas has been evaluated using machine learning methods like principal component regression (PCR), support vector regression (SVR), random forest regression (RF), and gradient boosting machine (GBM). In terms of performances, RF and GBM have performed much better than SVR and PCR. More robust and consistent results have been obtained for GBM during both training and testing periods.

Artificial neural network for flood susceptibility mapping in Bangladesh

The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.

A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.

A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.

Modelling flood susceptibility based on deep learning coupling with ensemble learning models

Modelling flood susceptibility is an indirect way to reduce the loss from flood disaster. Now, flood susceptibility modelling based on data driven model is state-of-the-art method such as ensemble learning and deep learning. However, the effect of deep learning coupling with ensemble learning models in flood susceptibility modelling is still unknown. Therefore, the aim of this paper is to propose three deep learning coupling with ensemble learning models by combining the deep learning (DL) with Filtered Classifier (FC), Rotation forest (RF) and Random Subspace (RSS) and explore the effect of coupling method for modelling flood susceptibility. The key step of this paper is as following: firstly, a Dingnan County which is lied in the Jiangxi Province of China is chosen as a case study, single flood event point and random sampling method was applied to generate the flood and non-flood data, respectively, then frequency ratio was utilized to analyze the relationship between each influencing factor and flood occurrence, based on the value of VIF, Spearman's correlation and One R classifier, the result show that there is no multicollinearity between each influencing factor, ten influencing factors have contribution to the flood occurrence and all of them are applied to construct the coupling model. Finally, the DL, FC-DL, RF-DL and RSS-DL were applied to produce flood susceptibility maps. Then, several statistical indexes such as area under the curve (AUC), Kappa index, accuracy (ACC), and F-measure were used to assess the accomplishment of these coupling models. For the train data, the FC-DL model acquired the highest AUC value (0.996), followed by RF-DL (0.944), RSS-DL (0.934), and DL (0.934). For the validation data, the result showed that all models have a good accomplishment (AUC>0.8). In a word, the deep learning coupling with ensemble learning models demonstrates the more reliable and excellent performance. Hence, the proposed new method will help the government for land use planning and can be applied in other area around the world.

An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires

Fires are one of the main disasters in underground engineering. In order to comprehensively describe and evaluate the risk of underground engineering fires, this study proposes a UEF risk assessment method based on EPB-FBN. Firstly, based on the EPB model, the static and dynamic information of the fire, such as the cause, occurrence, hazard, product, consequence, and emergency rescue, was analyzed. An EPB model of underground engineering fires was established, and the EPB model was transformed into a BN structure through the conversion rules. Secondly, a fuzzy number was used to describe the state of UEF variable nodes, and a fuzzy conditional probability table was established to describe the uncertain logical relationship between UEF nodes. In order to make full use of the expert knowledge and empirical data, the probability was divided into intervals, and a triangulated fuzzy number was used to represent the linguistic variables judged by experts. The α-weighted valuation method was used for de-fuzzification, and the exact conditional probability table parameters were obtained. Through fuzzy Bayesian inference, the key risk factors can be identified, the sensitivity value of key factors can be calculated, and the maximum risk chain can be found in the case of known evidence. Finally, the method was applied to the deductive analysis of three scenarios. The results show that the model can provide realistic analysis ideas for fire safety evaluation and emergency management of underground engineering. The proposed EPB risk assessment model provides a new perspective for the analysis of UEF accidents and contributes to the ongoing development of UEF research.

Assessing the urban road waterlogging risk to propose relative mitigation measures

Road waterlogging has become a significant issue in developed cities due to the rapid urbanization in China. It is necessary to accurately identify the risk of waterlogging in urban roads and propose appropriate mitigation measures. This study considered urban waterlogging as a landscape ecological process. The road waterlogging risk was simulated and estimated using the Minimum Cumulative Resistance model under natural drainage conditions. The results indicate that: 1) The Minimum Cumulative Resistance model effectively assesses the waterlogging risk for each road segment. The roads in and around the central city have relatively higher waterlogging risks. The overall length of high-risk roads is 918.7 km, accounting for 31.3 % of the total. 2) There are 448 potential runoff paths and 448 inflow sites. The city's center and its north and south sides are the primary locations of the high-risk runoff paths and the inflow sites. 3) Road waterlogging is significantly more affected by the land-use types of High density residential and Industrial under rainfall intensities of a-year, 2-year, 3-year, and 5-year return periods. And the effects of various land-use types on waterlogging vary with the rainfall intensity. Using landscape ecology theory to analyze the risk of road waterlogging is a novel method to address urban waterlogging issues. This approach provides a more accurate approach to identifying the urban waterlogging risks and can be applied to developed cities suffering from waterlogging to help decision-makers devise the most effective mitigation measures.

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.

LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China

Inland river runoff variations in arid regions play a decisive role in maintaining regional ecological stability. Observation data of inland river runoff in arid regions have short time series and imperfect attributes due to limitations in the terrain environment and other factors. These shortages not only restrict the accurate simulation of inland river runoff in arid regions significantly, but also influence scientific evaluation and management of the water resources of a basin in arid regions. In recent years, research and applications of machine learning and in-depth learning technologies in the hydrological field have been developing gradually around the world. However, the simulation accuracy is low, and it often has over-fitting phenomenon in previous studies due to influences of complicated characteristics such as “unsteady runoff”. Fortunately, the circulation layer of Long-Short Term Memory (LSTM) can explore time series information of runoffs deeply to avoid long-term dependence problems. In this study, the LSTM algorithm was introduced and improved based on the in-depth learning theory of artificial intelligence and relevant meteorological factors that were monitored by coupling runoffs. The runoff data of the Yarkant River was chosen for training and test of the LSTM model. The results demonstrated that Mean Absolute Error (MAE) and Root Mean Square error (RMSE) of the LSTM model were 3.633 and 7.337, respectively. This indicates that the prediction effect and accuracy of the LSTM model were significantly better than those of the convolution neural network (CNN), Decision Tree Regressor (DTR) and Random Forest (RF). Comparison of accuracy of different models made the research reliable. Hence, time series data was converted into a problem of supervised learning through LSTM in the present study. The improved LSTM model solved prediction difficulties in runoff data to some extent and it applied to hydrological simulation in arid regions under several climate scenarios. It not only decreased runoff prediction uncertainty brought by heterogeneity of climate models and increased inland river runoff prediction accuracy in arid regions, but also provided references to basin water resource management in arid regions. In particular, the LSTM model provides an effective solution to runoff simulation in regions with limited data.

Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.

Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.

Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

Purpose

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Design/methodology/approach

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

Findings

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

Originality/value

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R2 = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.

Boron removal from aqueous solutions by chitosan/functionalized-SWCNT-COOH: Development of optimization study using response surface methodology and simulated annealing

Boron contamination in water resources (especially drinking waters and agricultural land) is a major problem for the ecosystem. In this study, a novel synthesized chitosan/functionalized-SWCNT-COOH was prepared to separate boron (as boric acid) from aqueous solutions. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis revealed that SWCNT was dispersed in chitosan homogenously. Moreover, this study has related to the constrained optimization problem with an engineering approach. Response surface method (RSM) with face-centered central composite design (FCCCD) was chosen for maximizing the adsorption capacity as well as determining optimal independent factors such as pH, adsorbent dose, and concentration of boric acid. The optimized response (adsorption capacity) was reached 62.16 mg g^-1 under the optimal conditions (98.77 mg L^-1 of boric acid concentration, pH of 5.46 and 76 min). The present study has indicated that the synthesized material can be used as an adsorbent for eliminating boric acid from aqueous solutions depending on its high adsorbent capacity to remove boron and has better performance than existing adsorbents. Furthermore, simulated annealing (SA) optimization technique was used to compare the findings of RSM. Moreover, the selected optimization techniques were compared with error functions. The optimal conditions derived from SA were 91.17 mg L^-1 of boric acid concentration, pH of 5.86, and 76.17 min. The optimal adsorption capacity of SA was found to be 62.06 mg g^-1. These results revealed that the predictions of the two models are very close to each other.

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.

Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories

Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure and strongly affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density (PD), livestock density (LD), percentage of farmers and ranchers (PFR), distance to industrial and mining areas (DTIM), distance to tourist and cultural heritage areas (DTTCH), land use, distance to residential areas (DTRe), distance to road (DTR), and distance to stream (DTS)) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed, and the analytical network process (ANP) was applied. Finally, with the aim to overcome ambiguity, reduce uncertainty, and keep the data variability, an appropriate fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-fuzzy showed that about 19.2% of the region has a high to very high flood vulnerability.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Changes in floodplain regimes over Canada due to climate change impacts: Observations from CMIP6 models

With the recent Coupled Model Intercomparison Project Phase 6 (CMIP6), water experts and flood modellers are curious to explore the efficacy of the new and upgraded climate models in representing flood inundation dynamics and how they will be impacted in the future by climate change. In this study, for the first time, we consider the latest group of General Circulation Models (GCMs) from CMIP6 to examine the probable changes in floodplain regimes over Canada. A set of 17 GCMs from Shared Socioeconomic Pathways (SSPs) 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1980 to 2019), near-future (2021 to 2060), and far-future (2061 to 2100) time-periods are selected. A comprehensive framework consisting of hydrodynamic flood modelling, and statistical experiments are put forward to derive high-resolution Canada-wide floodplain maps for 100 and 200-yr return periods. The changes in floodplain regimes for the future periods are analyzed over drainage basin scale in terms of (i) changes in flood inundation extents, (ii) changes in flood hazards (high and very-high classes), and (iii) changes in flood frequency. Our results show a significant rise (>30%) in flood inundation extents in the future periods; particularly intense over western and eastern regions. The flood hazards are expected to cover ~16% more geographical area of Canada. We also find that large areas in northern and western Canada and a few spots in the eastern parts of Canada will be getting flooded more frequently compared to the historical period. The observations derived from this study are vital for enhancing flood preparedness, optimal land-use planning, and refurbishing both structural and non-structural flood control options for improved resilience. The study instills new knowledge on revamping the existing flood management approaches and adaptation strategies for future protection.

A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation. In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification. The methodology has been tested and statistically validated in an area of 14,3 km² located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.

Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning

Floods often significantly impact human lives, properties, and activities. Prioritizing areas in a region for mitigation based on flood probability is essential for reducing losses. In this study, two game theory (GT) algorithms - Borda and Condorcet - were used to determine the areas in the Tajan watershed, Iran that were most likely to flood, and two machine learning models - random forest (RF), and artificial neural network (ANN) - were used to model flood probability (the probability of flooding). Twelve independent variables (slope, aspect, elevation, topographic position index (TPI), topographic wetness index (TWI), terrain ruggedness index (TRI), land use, soil, lithology, rainfall, drainage density, and distance to river) and 263 locations of flooding were used to model and prepare flood-probability maps. The RF model was more accurate (AUC = 0.949) than the ANN model (AUC = 0.888). Frequency ratio (FR) was calculated for all factors to determine which had the most influence on flood probability. The values of twelve factors that affect flood probability were estimated for each sub-watershed. Then, game-theory algorithms were used to prioritize sub-watersheds in terms of flood probability. A pairwise comparison matrix revealed that the sub-watersheds most likely to flood. The Condorcet algorithm selected sub-watersheds 1, 2, 4, 5, and 11 and the Borda algorithm selected sub-watersheds 2, 4, 5, 20 and 11. Both models predicted that most of the watershed has very low flood probability and a very small portion has a high probability for flooding. The quantitative analysis and characterization of the watersheds from the perspective of flood hazard can support decision making, planning, and investment in mitigation measures.

Impact of wetland fragmentation due to damming on the linkages between water richness and ecosystem services

Evaluation of the importance of ecosystem services (ES) of various wetlands is well reported with global and regional level research, but the degree to which spatial-temporal variations in water richness (availability of water) have had an effect on ES has not yet been examined. The present work is intended to investigate the influence of wetland fragmentation due to damming on wetland water richness and the impact of changes in water richness on the ecosystem service value (ESV) of the wetland-dominated rivers of the lower Punarbhaba Basin, India, and Bangladesh, as the case. Water richness models of pre- and post-dam periods have been constructed based on four hydro-ecological parameters (hydro-period, depth of water, consistency of water appearance, and wetland size) following the semi-quantitative analytic hierarchy process (AHP). ESV of different wetland types, with and without considering water richness effect, has been computed. The result indicates that the overall wetland area decreased from 73,563 to 52,123 km² during the post-dam period. Approximately 53.8% of the high water-rich region is decreased. Total wetland ESV has been lowered by 63.4% from 1989 to 2019, with an average reduction rate of 2%. This is mainly due to the squeezing of the wetland area during the post-dam period. If the impact of water richness on ESV is considered, the scenario is found to be very distinct. Total ESV of various ESV areas amounted to $33 million during the pre-dam period and is reduced to $19.71 million during the post-dam period. If compared to the total ESV of the wetland without considering the effect of water richness, the calculated ESV gap was $105 million in pre-dam and $38 million in post-dam period indicating a widening of the gap. Maintaining the ES of wetland hydrological management, specifically the flow maintenance of river and riparian wetlands, is essential.

Integration of hard and soft supervised machine learning for flood susceptibility mapping

Flooding is a destructive natural phenomenon that causes many casualties and property losses in different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce the risk of this hazard, and has become the main approach in flood risk management. In this study, we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft supervised machine learning classification in FSM by using three ANN algorithms (multi-layer perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)). We used integration of these models for predicting the spatial expansion probability of flood events in the Ajichay river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, land use, rainfall, and distance to the river). The FSMs obtained as model outputs were trained and tested using flood inventory datasets earned based on previous records of flood damage in the region for the Ajichay river basin. Sensitivity analysis using one factor-at-a-time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a positive impact on modeling to generate FSM, with altitude having the greatest impact and curvature the least. Model validation was carried out using total operating characteristic (TOC) with an area under the curve (AUC). The highest success rate was found for MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation of FSMs produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. While integration of hard and soft supervised machine learning classification with activation functions of MLP-S and MLP-L showed a strong flood prediction capability for proper planning and management of flood hazards, MLP-S is a promising method for predicting the spatial expansion probability of flood events.

Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms

The transient storage model (TSM) is a common approach to assess solute transport and pollution modeling in rivers. Several formulas have been developed to estimate TSM parameters. This study develops a new hybrid optimization algorithm consisting of the dragonfly algorithm and simulated annealing (DA-SA) algorithms. This robust method provides accurate formulas for estimating TSM parameters (e.g., k_f, T, [Formula: see text]). A dataset gathered by previous scholars from several rivers in the USA was used to assess the proposed formulas based on several error metrics ([Formula: see text] and [Formula: see text]) and visual indicators. According to the results, DA-SA-based formulas adequately estimated the [Formula: see text] ([Formula: see text], [Formula: see text]), [Formula: see text] ([Formula: see text] [Formula: see text]), and [Formula: see text] ([Formula: see text] [Formula: see text]) parameters. Moreover, the DA-SA-1 showed higher accuracy by improving the RMSE and MAE by 98% compared to the DA and DA-SA-1 as alternatives. The formulas developed in this study significantly outperformed the results of previously proposed models by enhancing the NSE up to 70%. The hybrid DA-SA algorithm method proved highly reliable models to estimate the TSM parameters in the water pollution routing problem, which is vital for reactive solute uptake in advective and transient storage zones of stream ecosystems.

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree

Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang County, in China, by integrating two independent models of frequency ratio and index of entropy with multilayer perceptron and classification and regression tree models. The flood locations of the study area were identified through the flood inventory process, and 12 flood conditioning factors were used in the training and validation processes. According to the results of the linear support vector machine, elevation, slope angle, and soil have the highest predictive ability. The experimental results of the four hybrid models demonstrate that between 20% and 50% of the study area has high and very high flood susceptibility. The multilayer perceptron-probability density hybrid model is the most effective among the six comparative methods.

Feature selection algorithm for usability engineering: a nature inspired approach

Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

Deep Learning Based Approach to Classify Saline Particles in Sea Water

Water is an essential resource that facilitates the existence of human life forms. In recent years, the demand for the consumption of freshwater has substantially increased. Seawater contains a high concentration of salt particles and salinity, making it unfit for consumption and domestic use. Water treatment plants used to treat seawater are less efficient and reliable. Deep learning systems can prove to be efficient and highly accurate in analyzing salt particles in seawater with higher efficiency that can improve the performance of water treatment plants. Therefore, this work classified different concentrations of salt particles in water using convolutional neural networks with the implementation of transfer learning. Salt salinity concentration images were captured using a designed Raspberry Pi based model and these images were further used for training purposes. Moreover, a data augmentation technique was also employed for the state-of-the-art results. Finally, a deep learning neural network was used to classify saline particles of varied concentration range images. The experimental results show that the proposed approach exhibited superior outcomes by achieving an overall accuracy of 90% and f-score of 87% in classifying salt particles. The proposed model was also evaluated using other evaluation metrics such as precision, recall, and specificity, and showed robust results.

Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments

In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.

Susceptibility mapping of groundwater salinity using machine learning models

Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning (ML) models in groundwater salinity mapping based on the dichotomous predictions. The groundwater salinity is predicted using the essential factors (i.e., identified by the simulated annealing feature selection methodology) through k-fold cross-validation methodology. Six ML models, namely, flexible discriminant analysis (FDA), mixture discriminant analysis (MAD), boosted regression tree (BRT), multivariate adaptive regression spline (MARS), random forest (RF), support vector machine (SVM), were employed to groundwater salinity mapping. The results of the modeling indicated that the SVM model had superior performance than other models. Variables of soil order, groundwater withdrawal, precipitation, land use, and elevation had the most contribute to groundwater salinity mapping. Results highlighted that the southern parts of the region and some parts in the north, northeast, and west have a high groundwater salinity, in which these areas are mostly matched with soil order of Entisols, bareland areas, and low elevations.

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

Drivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inundation consequences under the influence of various factors. In this paper, urban flood inundation risk assessment model is established based on Bayesian network, and then key disaster-causing factors chains are explored through influence strength analysis. Jingdezhen City is selected as study area, where the flood inundation probability is calculated, and the paths of these influential factors are found. The results show that the probability of inundation in most areas is low. Risk greater than 0.8 account for about 9%, and most of these areas are located in the middle and southern section of the city. The influencing factors interact with each other in the form of factor chain and, finally, affect the flood inundation. Rainfall directly affects inundation, while river is the key factor on inundation which is influenced by elevation and slope. In addition, in the chain of socio-economic factors, the population will determine the pipe density through affecting gross domestic product (GDP), and lead to the inundation. The approach proposed in this study can be used to find key disaster-causing factors chains, which not only quantitatively reveal the formation of risks but also provide reference for early warning.

A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.

Asthma-prone areas modeling using a machine learning model

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O₃), sulfur dioxide (SO₂), carbon monoxide (CO), and nitrogen dioxide (NO₂). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPI_DLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

Modeling Flash Floods and Induced Recharge into Alluvial Aquifers Using Multi-Temporal Remote Sensing and Electrical Resistivity Imaging

Flash flood hazard assessments, mitigation measures, and water harvesting efforts in desert environments are often challenged by data scarcity on the basin scale. The present study, using the Wadi Atfeh catchment as a test site, integrates remote sensing datasets with field and geoelectrical measurements to assess flash flood hazards, suggest mitigation measures, and to examine the recharge to the alluvium aquifer. The estimated peak discharge of the 13 March 2020 flood event was 97 m3/h, which exceeded the capacity of the culverts beneath the Eastern Military Highway (64 m3/h), and a new dam was suggested, where 75% of the catchment could be controlled. The monitoring of water infiltration into the alluvium aquifer using time-lapse electrical resistivity measurements along a fixed profile showed a limited connection between the wetted surficial sediments and the water table. Throughflow is probably the main source of recharge to the aquifer rather than vertical infiltration at the basin outlet. The findings suggest further measures to avoid the negative impacts of flash floods at the Wadi Atfeh catchment and similar basins in the Eastern Desert of Egypt. Furthermore, future hydrological studies in desert environments should take into consideration the major role of the throughflow in alluvium aquifer recharge.

Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran

The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use/land cover (LU/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas.

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater.