Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Multi-hazard susceptibility model based on high spatial resolution data-a case study of Sali settlement (Dugi otok, Croatia)

The world has been facing an increase in various natural hazards. The coastal regions are recognized as one of the most vulnerable due to high population pressure and climate change intensity. Mediterranean countries have one of the most burnable ecosystems in the world, one of the most exposed to pluvial floods, and have the highest erosion rates within the EU. Therefore, the aim of this study was to develop the first multihazard susceptibility model in Croatia for the Sali settlement (island of Dugi otok). The creation of a multi-hazard susceptibility model (MHSM) combined the application of geospatial technology (GST) with a local perception survey. The methodology consisted of two main steps: (1) creating individual hazard susceptibility models (soil erosion, wildfires, pluvial floods), and (2) overall hazard susceptibility modeling. Multicriterial GIS analyses and the Analytical Hierarchy Process were used to create individual hazard models. Criteria used (32) to create models are derived from very-high-resolution (VHR) models. Two versions of MHSM are created: 1) all criteria with equal weighting coefficients and 2) weight coefficients determined based on public perception. According to MHSM 1, most of the research (58%) area is moderately susceptible to multiple hazards. Highly and very highly susceptible areas are 27% of the drainage basin and are mostly located near roads and houses. MHSM 2 reveals similar results to MHSM 1. The public perceives that the research area is the most susceptible to wildfires. The wildfire ignition risk is ranked as moderate (3.00) with a standard deviation of 1.16. Pluvial flood risk is ranked low (2.78), with a standard deviation of 1.15. The risk of soil is most inferior (2.24) with a standard deviation of 0.91. The the most significant difference between public perception and the GIS-MCDA model of hazard susceptibility is related to soil erosion. However, the accuracy of the soil erosion model was confirmed by ROC curves based on recent traces of soil erosion in the research area. The proposed methodological framework of multi-hazard susceptibility modeling can be applied, with minor modifications, to other Mediterranean countries.

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.

Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping

Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management.

Time traveling through the floodscape: assessing the spatial and temporal probability of floods and susceptibility zones in the Lower Damodar Basin

The flood of Damodar river is a well-known fact which is used to the whole riverine society of the basin as well as to the eastern India. The study aims to estimate the spatio-temporal probability of floods and identify susceptible zones in the Lower Damodar Basin (LDB). A flood frequency analysis around 90 years hydrological series is performed using the Log-Pearson Type III model. The frequency ratio model has also been applied to determine the spatial context of flood. This reveals the extent to which the LDB could be inundated in response to peak discharge conditions, especially during the monsoon season. The findings indicate that 36.64% of the LDB falls under high to very high flood susceptibility categories, revealing an increasing downstream flood vulnerability trend. Hydro-geomorphic factors substantially contribute to the susceptibility of the LDB to high magnitude floods. A significant shift in flood recurrence intervals, from biennial occurrences in the pre-dam period to decadal or vicennial occurrences in the post-dam period, is observed. Despite a reduction in high-magnitude flood incidents due to dam and barrage construction, irregular flood events persist. The effect of flood in the LDB region is considered to be either positive as well as negative in terms of wholistic sense and impact. The analytical results of this research could serve to identify flood-prone zones and guide the development of flood resilience policies, thereby promoting sustainability within the LDB floodplain.

Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique-a case study of Patan district Gujarat, India

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

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.

Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation.

Flood susceptibility mapping by best-worst and logistic regression methods in Mersin, Turkey

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Flood Hazard Zoning of Upper Awash River Basin, Ethiopia, Using the Analytical Hierarchy Process (AHP) as Compared to Sensitivity Analysis

Floods and droughts have been two of the most devastating consequences of the climate crisis affecting billions of people in the world. However, unlike the other natural hazards, flooding is manageable through appropriate flood management mechanisms. This study emphasizes on developing a flood hazard zone for the Upper Awash River Basin (UARB), Ethiopia. Six relevant climate, physiographic, and biophysical factors were considered. Then, a flood hazard map was developed employing the analytic hierarchy process (AHP) method and further validated using sensitivity analysis and collected flood marks. The results revealed that drainage density, rainfall, and elevation have higher significance, while land use and soil permeability have a low impact in the process of flood generation. The map showed vulnerable areas at different levels and can serve as a valuable input for the decision makers to consider in the process of implementing emergency plans as well as long-term flood mitigation options.

A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen's kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district's relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

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.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management

The present study intends to improve the robustness of a flood susceptibility (FS) model with a small number of parameters in data-scarce areas, such as northwest Bangladesh, by employing machine learning-based sensitivity analysis and an analytical hierarchy process (AHP). In this study, the nine most relevant flood elements (such as distance from the river, rainfall, and drainage density) were chosen as flood conditioning variables for modeling. The FS model was produced using AHP technique. We used an empirical and binormal receiver operating characteristic (ROC) curves for validating the models. We performed Sensitivity analyses using a random forest (RF)-based mean Gini decline (MGD), mean decrease accuracy (MDA), and information gain ratio to find out the sensitive flood conditioning variables. After performing sensitivity analysis, the least sensitivity variables were eliminated. We re-ran the model with the rest of the parameters to enhance the model’s performance. Based on previous studies and the AHP weighting approach, the general soil type, rainfall, distance from river/canal (Dr), and land use/land cover (LULC) had higher factor weights of 0.22, 0.21, 0.19, and 0.15, respectively. The FS model without sensitivity and with sensitivity performed well in the present study. According to the RF-based sensitivity and information gain ratio, the most sensitive factors were rainfall, soil type, slope, and elevation, while curvature and drainage density were less sensitive parameters, which were excluded in re-running the FS model with just vital parameters. Using empirical and binormal ROC curves, the new FS model yields higher AUCs of 0.835 and 0.822, respectively. It is discovered that the predicted model’s robustness may be maintained or increased by removing less relevant factors. This study will aid decision-makers in developing flood management plans for the examined region.

Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Threats of climate change and land use patterns enhance the susceptibility of future floods in India

The main objective of this work is the future prediction of the floods in India due to climate and land change. Human activity and related carbon emissions are the primary cause of land use and climate change, which has a substantial impact on extreme weather conditions, such as floods. This study presents high-resolution flood susceptibility maps of different future periods (up to 2100) using a combination of remote sensing data and GIS modelling. To quantify the future flood susceptibility various flood causative factors, Global circulation model (GCM) rainfall and land use and land cover (LULC) data are envisaged. The present flood susceptibility model has been evaluated through receiver operating characteristic (ROC) curve, where area under curve (AUC) value shows the 91.57% accuracy of this flood susceptibility model and it can be used for future flood susceptibility modelling. Based on the projected LULC, rainfall and flood susceptibility, the results of the study indicating maximum monthly rainfall will increase by approximately 40-50 mm in 2100, while the conversion of natural vegetation to agricultural and built-up land is about 0.071 million sq. km. and the severe flood event area will increase by up to 122% (0.15 million sq. km) from now on.

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment in allowing a comprehensive understanding of the current situation, readiness, preparation, resources, and others for decision assessment and disaster events planning. This study aims to facilitate the criteria identification and selection from a macro domain perspective in improving flood management planning. The objectives of this study are (a) to explore and identify potential and possible criteria to be incorporated in the current flood management plan in the macro domain perspective; (b) to understand the type of flood measures and decision goals implemented to facilitate flood management planning decisions; and (c) to examine the possible structured mechanism for criteria selection based on the decision analysis technique. Based on a systematic literature review and thematic analysis using the PESTEL framework, the findings have identified and clustered domains and their criteria to be considered and applied in future flood management plans. The critical review on flood measures and decision goals would potentially equip stakeholders and policy makers for better decision making based on a disaster management plan. The decision analysis technique as a structured mechanism would significantly improve criteria identification and selection for comprehensive and collective decisions. The findings from this study could further improve Malaysia Adaptation Index (MAIN) criteria identification and selection, which could be the complementary and supporting reference in managing flood disaster management. A proposed framework from this study can be used as guidance in dealing with and optimising the criteria based on challenges and the current application of Big Data and criteria in managing disaster events.

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.

Data-driven flood hazard zonation of Italy

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran

We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.

An Overview of Multi-Criteria Decision Analysis (MCDA) Application in Managing Water-Related Disaster Events: Analyzing 20 Years of Literature for Flood and Drought Events

This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters.

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.

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.

GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan

Floods are considered one of the world’s most overwhelming hydro meteorological disasters, which cause tremendous environmental and socioeconomic damages in a developing country such as Pakistan. In this study, we use a Geographic information system (GIS)-based multi-criteria approach to access detailed flood vulnerability in the District Shangla by incorporating the physical, socioeconomic vulnerabilities, and coping capacity. In the first step, 21 essential criteria were chosen under three vulnerability components. To support the analytical hierarchy process (AHP), the used criteria were transformed, weighted, and standardized into spatial thematic layers. Then a weighted overlay technique was used to build an individual map of vulnerability components. Finally, the integrated vulnerability map has been generated from the individual maps and spatial dimensions of vulnerability levels have been identified successfully. The results demonstrated that 25% of the western-middle area to the northern part of the study area comprises high to very high vulnerability because of the proximity to waterways, high precipitation, elevation, and other socioeconomic factors. Although, by integrating the coping capacity, the western-central and northern parts of the study area comprising from high to very high vulnerability. The coping capacities of the central and eastern areas are higher as compared to the northern and southern parts of the study area because of the numerous flood shelters and health complexes. A qualitative approach from the field validated the results of this study. This study’s outcomes would help disaster managers, decision makers, and local administration to quantify the spatial vulnerability of flood and establish successful mitigation plans and strategies for flood risk assessment in the study area.

An Overview of Flood Risk Analysis Methods

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.

Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment

Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results.

Flood Risk Assessment for the Long-Term Strategic Planning Considering the Placement of Industrial Parks in Slovakia

The intention of the article is to demonstrate how data from historical maps might be applied in the process of flood risk assessment in peri-urban zones located in floodplains and be complementary datasets to the national flood maps. The research took place in two industrial parks near the rivers Žitava and Nitra in the town of Vráble (the oldest industrial park in Slovakia) and the city of Nitra (one of the largest industrial parks in Slovakia, which is still under construction concerning the Jaguar Land Rover facility). The historical maps from the latter half of the 18th and 19th centuries and from the 1950s of the 20th century, as well as the field data on floods gained with the GNSSS receiver in 2010 and the Q100 flood line of the national flood maps (2017), were superposed in geographic information systems. The flood map consists of water flow simulation by a mathematical hydrodynamic model which is valid only for the current watercourse. The comparison of historical datasets with current data indicated various transformations and shifts of the riverbanks over the last 250 years. The results proved that the industrial parks were built up on traditionally and extensively used meadows and pastures through which branched rivers flowed in the past. Recent industrial constructions intensified the use of both territories and led to the modifications of riverbeds and shortening of the watercourse length. Consequently, the river flow energy increased, and floods occurred during torrential events in 2010. If historical maps were respected in the creation of the flood maps, the planned construction of industrial parks in floodplains could be limited or forbidden in the spatial planning documentation. This study confirmed that the flood modelling using the Q100 flood lines does not provide sufficient arguments for investment development groups, and flood maps might be supplied with the data derived from historical maps. The proposed methodology represents a simple, low cost, and effective way of identifying possible flood-prone areas and preventing economic losses and other damages.

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

The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Flood Risk Assessment of Global Watersheds Based on Multiple Machine Learning Models

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.