Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery

Floods are the natural disaster that occurs most frequently due to the weather and causes the most widespread destruction. The purpose of the proposed research is to analyze flood susceptibility mapping (FSM) in the Sulaymaniyah province of Iraq. This study employed a genetic algorithm (GA) to fine-tune parallel ensemble-based machine learning algorithms (random forest (RF) and bootstrap aggregation (Bagging)). Four machine learning algorithms (RF, Bagging, RF-GA, and Bagging-GA) were used to build FSM in the study area. To provide inputs into parallel ensemble-based machine learning algorithms, we gathered and processed data from meteorological (Rainfall), satellite image (flood inventory, normalized difference vegetation index (NDVI), aspect, land cover, altitude, stream power index (SPI), plan curvature, topographic wetness index (TWI), slope) and geographic sources (geology). For this research, Sentinel-1 synthetic aperture radar (SAR) satellite images were utilized to locate flooded areas and create an inventory map of floods. To train and validate the model, we employed 70 % and 30 % of 160 selected flood locations, respectively. Multicollinearity, frequency ratio (FR), and Geodetector methods were used for data preprocessing. Four metrics were utilized to assess the FSM performance: the root mean square error (RMSE), the area under the receiver-operator characteristic curve (AUC-ROC), the Taylor diagram, and the seed cell area index (SCAI). The results exhibited that all the suggested models have high accuracy of prediction, but the performance of Bagging-GA (RMSE (Train = 0.1793, Test = 0.4543)) was slightly better than RF-GA (RMSE (Train = 0.1803, Test = 0.4563)), Bagging (RMSE (Train = 0.2191, Test = 0.4566)), and RF (RMSE (Train = 0.2529, Test = 0.4724)). According to the ROC index, the Bagging-GA model (AUC = 0.935) was the most accurate in flood susceptibility modeling, followed by the RF-GA (AUC = 0.904), the Bagging (AUC = 0.872), and the RF (AUC = 0.847) models. The study's identification of high-risk flood zones and the most significant factors contributing to flooding make it a helpful resource for flood management.

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and extreme weather prediction. Tanzanian river basins currently lack reliable mechanisms for accurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction, the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems.

How far the types and wetland hydrological conditions influence its provisioning services in the Indian mature Ganges delta

Present work intended to explore how far the Provisioning Service Value (PSV) of the mature Ganges deltaic wetlands is determined by its typology and a few physical attributes like hydrology and aquatic vegetations. Firstly, a field investigation was carried out in the representative sample sites, and field-measured PSV was calibrated with wetland types, hydrological security, and aquatic plant biomass to perform spatial estimation and mapping of PSV. The estimation yielded average annual PSV of entire wetlands as 146.5 × 10⁵ Indian Rupee (INR)/km²/year, with the highest over bheries (embankments for fish and shrimp aquaculture) 176 × 10⁵ INR/km²/year and lowest over marshy wetlands 107 × 10⁵ INR/km²/year. Sensitivity analysis of this estimation showed in cases of 55% field visited sites, the field-measured PSV was outside the range of low standard regression residuals (-0.5 to 0.5). While searching for the reason behind such error in the estimation, the variability of the field-measured PSV was measured. Various inequality measures showed high inequality in inter and intra-hydrological conditions of the wetland. Analysis of variance (ANOVA) proved statistical significance of within-class variability. To explain the variability of PSV, Kernel Density Estimation (KDE) plotting was performed, incorporating a few other regional conditioning factors like wetland size, fish and shrimp aquaculture, perenniality, expenditure, and external feeding from the experience of the field. From this excesize, external feeding and expenditure were essential factors that should be incorporated along with the wetland characteristics and physical attributes for accurate estimation. Since producing spatial data layers of these factors with a finer resolution is difficult, the study suggests case-specific estimation of PSV instead of general spatial mapping.

Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia

The present study is focused on the flood inundation in Brahmaputra Basin, which is one of the most recurrent and destructive natural disasters of the region. The flood inundation was assessed using C-Band Sentinel 1A synthetic aperture radar (SAR) during 2015–2020 with precipitation patterns, runoff discharge, and their impacts on land cover in the basin. The study exhibited a very high precipitation during monsoon in the upper catchment resulting in severe flood inundation in downslopes of Brahmaputra Basin. A very high (900–2000 mm) to extremely high (>2000 mm) monthly cumulative precipitation in the south and south-eastern parts of basin led to high discharge (16,000 to 18,000 m³s^-1) during July-August months. The river discharge increases with cumulative effects of precipitation and melting of snow cover during late summer and monsoon season, and induced flood inundation in lower parts of basin. This flood has largely affected agricultural land (>77% of total basin), forests (~3%), and settlement (426 to 1758 km²) affecting large wildlife and livelihood during 2015–2020. The study highlights the regions affected with recurrent flood and necessitates adopting an integrated, multi-hazard, multi-stakeholder approach with an emphasis on self-reliance of the community for sustenance with local resources and practices.

High-Resolution Hydrological-Hydraulic Modeling of Urban Floods Using InfoWorks ICM

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the Probability Distributed Moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km2 to 3.61 km2 as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.

Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia

Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.

Examining the Impact of Different DEM Sources and Geomorphology on Flash Flood Analysis in Hyper-Arid Deserts

Digital elevation models (DEMs) are the cornerstone for hydrological and geomorphological modeling. Herein, two Nile-tributary catchments (Wadi Al Rishrash and Wadi Atfeh) in Egypt are selected to examine the contribution of different DEMs to the accuracy of hydrological and geomorphological analyses in the hyper-arid Sahara. DEMs sources include: Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) (12.5 m resolution), ALOS World 3D with 30 m resolution (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM with 30 m resolution) and the Shuttle Radar Topography Mission (SRTM with 30 and 90 m resolution), in addition to topographic map-derived DEM (90 m resolution). Using a hypothetical uniformly-distributed 10 mm rainfall event, the estimated parameters, including: flow duration, time to peak and peak discharge rates, are almost similar for the different DEMs and thus technical aspects related to sources and resolutions of the datasets impose insignificant control on quantitative flash-flood analyses. Conversely, variations in geological and geomorphological characteristics of the catchments show more significant control on the hydrograph magnitudes as indicated by the different parameters of the two catchments. These findings indicate that understanding the geological and hydrological evolution of the catchment is essential for integrated management strategies of floods especially in the Saharan–Arabian deserts and in similar conditions of hyper-aridity and scarce in situ data worldwide.

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

Impacts of Land Use and Land Cover Changes on PeakDischarge and Flow Volume in Kakia and Esamburmbur Sub-Catchments of Narok Town, Kenya

Due to population growth and an expanding economy, land use/land cover (LULC) change is continuously intensifying and its effects on floods in Kakia and Esamburmbur sub-catchments in Narok town, Kenya, are increasing. This study was carried out in order to evaluate the influence of LULC changes on peak discharge and flow volume in the aforementioned areas. The Event-Based Approach for Small and Ungauged Basins (EBA4SUB) rainfall–runoff model was used to evaluate the peak discharge and flow volume under different assumed scenarios of LULC that were projected starting from a diachronic analysis of satellite images of 1985 and 2019. EBA4SUB simulation demonstrated how the configuration and composition of LULC affect peak discharge and flow volume in the selected catchments. The results showed that the peak discharge and flow volume are affected by the variation of the Curve Number (CN) value that is dependent on the assumed LULC scenario. The evaluated peak discharge and flow volume for the assumed LULC scenarios can be used by local Municipal bodies to mitigate floods.

A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

Delineation of groundwater quality locations suitable for target end-use purposes through deep neural network models

Groundwater is the main source of water for beverages, and its quality varies depending on extraction location; this is particularly the case in regions with complex geology, topography, and multiple forms of land use. Thus, it is important to determine a suitable groundwater extraction location based on intended water use and the related water quality standards. In this study, deep neural network (DNN) models and GIS data relating to groundwater quality were applied to estimate potential maps of Gangwon Province in South Korea, where groundwater is frequently extracted for drinking purposes. These maps specify areas where the groundwater quality is conducive for being used as mineral water and water for brewing coffee (hereafter referred as "coffee water"). Sensitivity analysis identified how inputs were sensitive to model estimation and showed that land-use variables were the most sensitive. The importance of each variable quantified how good or bad its region is for the desired groundwater. The overall features of importance were similar between mineral water and coffee water. However, with differences in hydrogeological units, carbonate rock was a variable of high positive importance for mineral water; metamorphic rock was its equivalent for coffee water. Our results offer a potential map of desired groundwater quality in the absence of a detailed understanding of the underlying hydrochemical processes governing groundwater quality. Additionally, the development of such a potential mapping model can help to determine the appropriate development area of groundwater for their respective purposes.

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam

Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.