Flood Susceptibility Modeling Research Articles

Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

Flood susceptibility and flood frequency modeling for lower Kosi Basin, India using AHP and Sentinel-1 SAR data in geospatial environment

Flood susceptibility and flood frequency modeling for lower Kosi Basin, India using AHP and Sentinel-1 SAR data in geospatial environment

Flood susceptibility modeling by integrating tree‐based regression with metaheuristic algorithm, BWO

AbstractFloods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.

Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas

Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas

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.

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.

Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches.

Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions

In recent years, there has been a growing interest in flood susceptibility modeling. In this study, we conducted a bibliometric analysis followed by a meta-data analysis to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in flood susceptibility modeling. Relevant publications were retrieved from the Web of Science database to identify the leading authors, influential journals, and trending articles. The results of the meta-data analysis indicated that hybrid models were the most frequently used prediction models. Results of bibliometric analysis show that GIS, machine learning, statistical models, and the analytical hierarchy process were the central focuses of this research area. The analysis also revealed that slope, elevation, and distance from the river are the most commonly used factors in flood susceptibility modeling. The present study discussed the importance of the resolution of input data, the size and representation of the training sample, other lessons learned, and future research directions in this field.

Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

AbstractClimate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.

An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping

Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood sampling should not be completely random. To represent the impact of the spatial dimension, this study proposed a new sampling approach based on spatial dependence, called inverse-occurrence sampling. It selects more non-flood data in low-risk areas than in high-risk areas. The new sampling approach was compared with random and stratified sampling, using six machine learning techniques in two urban areas in Guangzhou, China, with distinct flood mechanisms, that is, Tianhe (flood density 1.5/km2, clustered distribution, average slope 9.02°, downtown district) and Panyu (flood density 0.15/km2, random distribution, average slope 4.55°, suburban district). Learning techniques include support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNN-SVM, and CNN-RF. The main findings of this study were as follows: (1) Sampling approaches had a greater impact on model performance than learning techniques in terms of area under the receiver operating characteristic curve (AUC). The AUC variations caused by learning techniques ranged from 0.04 to 0.09. Meanwhile, the AUC variations caused by sampling approaches were between 0.15 and 0.22, all larger than 0.1. (2) The new sampling approach outperformed that of the other two sampling approaches for high average AUC values and small AUC variations. The outperformance is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group had a narrower range (0.14–0.18 in Tianhe and 0.35–0.39 in Panyu) than in the random group (0.24–0.28 in Tianhe and 0.43–0.53 in Panyu) and the stratified group (0.23–0.30 in Tianhe and 0.42–0.48 in Panyu). (3) The most accurate learning technique for AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN. (4) ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Flood density outliers tended to appear in the models derived using RF and CNN-RF. Finally, the newly proposed sampling approach is suggested to be applied to flood susceptibility mapping to reflect the impact of spatial dependence.

Flood susceptibility modeling using geo-morphometric ranking approach in Jhelum River basin, Pakistan

This study is modeling flood susceptibility in Jhelum River Basin by employing Geo-Morphometric ranking approach. The study area is located in the Monsoon region and exposed to heavy rainfall events. Every year monsoon rainfall and melting of snow and glaciers together generate flash floods in the upper catchment and then riverine flood in the low-lying areas. Watershed modeling approach is used to delineate the Jhelum watershed and its sub-watershed. Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is utilized as input data in ArcMap software environment. Then Geo-Morphometric ranking model is applied to rank the susceptibility of each sub-watershed to flood. The analysis showed that geo-morphometric characteristics vary from basin to basin. There are 44 sub-basins which cover area less than 1000 ​km2. The area of sub-basins ranges from 47.68 ​km2 to 7667.77 ​km2. Based on our results, area of sub-basin directly impacts flood susceptibility. Geo-morphometric parameters such as gradient, relief and geology of basin such as type of bed rock govern the drainage pattern. The results of the study can assist line agencies and concerned government departments to design location specific flood risk reduction strategy.

A comparative evaluation of GIS based flood susceptibility models: a case of Kopai river basin, Eastern India

A comparative evaluation of GIS based flood susceptibility models: a case of Kopai river basin, Eastern India

A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping

Flash floods are one of the worst natural disasters, causing massive economic losses and many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk of flooding is a crucial non-structural solution for managing floods. This study aimed to assess the efficacy of combinations of the random forest (RF) model with three biology-inspired metaheuristic algorithms, namely invasive weed optimization (IWO), slime mould algorithm (SMA), and satin bowerbird optimization (SBO), for flood susceptibility mapping in Estahban town, Iran. Initially, synthetic-aperture radar (SAR) (Sentinel-1) and optical (Landsat-8) satellite images were integrated to monitor the flooded areas during the July 2022 monsoon in the study area. A dataset of 509 flood occurrence points was created to identify flood-prone areas using remote sensing techniques, considering the monitored flood areas. The dataset also included twelve flood-related criteria: topography, land cover, and climate. The holdout method was employed for modeling, with a ratio of 70:30 used for the train/test split. Data pre-processing techniques were conducted to improve model performance, including determining criteria importance and addressing multicollinearity issues using certainty factor (CF), multicollinearity, and information gain ratio (IGR) methods. Then FSM was prepared using RF, RF-IWO, RF-SBO, and RF-SMA models. The findings of this research revealed that the RF-IWO model was the best predictive model of flood susceptibility modeling, with root-mean-square-error (RMSE) (0.211 and 0.0.27), mean-absolute-error (MAE) (0.103 and 0.15), and coefficient-of-determination (R2) (0.821 and 0.707) in the training and testing phases, respectively. Receiver operating characteristic (ROC) curve analysis of FSM revealed that the most accurate models were the RF-IWO (area under the curve (AUC) = 0.983), RF-SBO (AUC = 0.979), RF-SMA (AUC = 0.963), and RF (AUC = 0.959), respectively. Integrating biology-inspired computing algorithms with machine learning algorithms presents a novel approach to enhancing the accuracy of FSMs.

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.

Modeling and Mapping of Flood Susceptibility at Que Son District, Quang Nam Province, Vietnam using CatBoost

In this research, the main objective is to model and map flood susceptibility in Que Son district, Quang Nam province, Vietnam using one of the effective machine learning model namely CatBoost. With this purpose, a total of 96 flood and non-flood locations and a set of 10 conditioning factors were collected to construct the geospatial database. Thereafter, Shap feature importance method was used to validate and select the most important conditioning factors used for modeling of flood susceptibility, and the results showed that only 8 conditioning factors including aspect, slope, curvature, elevation, land cover, rainfall, distance to rivers, and Topographic Wetness Index (TWI) were selected for final modelling of flood susceptibility at the study area. Validation of the model was also done using various statistical indexes including area under the ROC curve (AUC). Validation results showed that the performance of CatBoost model (AUC = 0.96 for training and AUC = 0.94 for testing) is good for prediction of flood susceptibility of the study area. Thus, it can be concluded that CatBoost is valuable tool for flood susceptibility modeling which can be used to assess flood susceptibility in other flood prone areas of the world. In addition, flood susceptibility map generated from CatBoost model in this study might be helpful in development of better flood mitigation strategies at the study area.

Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam

A flood is a natural catastrophe that causes heavy damage not only to people but also to properties. To prevent and mitigate flood damage, an accurate flood susceptibility map that reveals highly potential flood-prone areas is essential. This study aims to construct flood susceptibility maps in the Huong Khe district using three machine learning algorithms, namely the K - Nearest Neighbour (KNN), the Support Vector Machine (SVM) and Artificial Neural Network (ANN). Training and testing datasets were extracted from Sentinel-1 SAR images. Seven causative factors were selected as input for predictive models after removing high-correlation factors and unimportant factors through a rigorous screening process by analyzing the Pearson correlation coefficient (PCC) and calculating the information gain ratio (InGR). The model's hyperparameters were found by grid search algorithm integrated 5-fold cross-validation. The three optimal flood susceptibility models showed excellent performance, with very high accuracy indices in the training and testing phases, over 90% of overall accuracy and UAC values. High and very high susceptibility classes on flood susceptibility maps accounted for around 18% of the total study area and were mainly located in residential and agricultural areas. Thus, there is a need to make proper land use planning for these areas to reduce damage in flood seasons.

Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment

Flash floods are among the world most destructive natural disasters, and developing optimum hybrid Machine Learning (ML) models for flash flood susceptibility (FFS) modeling remains a challenge. This study proposed novel intelligence algorithms based on a hybrid of several ensemble ML models (i.e., Bagged Flexible Discriminant Analysis (BAFDA), Extreme Gradient Boosting (XBG), Rotation Forest (ROF) and Boosted Generalized Additive Model (BGAM)) and wrapper-based factor optimization algorithms (i.e., Recursive Feature Elimination (RFE) and Boruta) to improve the accuracy of FFS mapping at Neka-Haraz watershed in Iran. In addition, the Random Search (RS) method is proposed for meta-optimization of the developed hybrid models hyper-parameters. This study considers 20 flash flood conditioning factors (CgFs) and 380 flood and non-flood locations to create a geospatial database. The performance of each hybrid model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and several validation methods, such as efficiency. The developed hybrid models demonstrated good performance, with BGAM-Boruta achieving the highest performance (AUC = 0.953, and Efficiency = 0.910), followed by ROF-Boruta (AUC = 0.952), ROF-RFE (AUC = 0.951), BAFDA-Boruta (AUC = 0.950), BGAM-RFE (AUC = 0.950), ROF (AUC = 0.949), BGAM (AUC = 0.948), BAFDA-RFE (AUC = 0.943), XGB-Boruta (AUC = 0.943), BAFDA (AUC = 0.939), XGB-RFE (AUC = 0.938) and XGB (AUC = 0.911). In the BGAM-Boruta model, the regional coverage was about 46% for high to very high FFS areas. Moreover, the study revealed that distance to river, slope, rainfall, altitude, and distance to road CgFs are the most significant for FFS modeling in this region.

Application of Morphometric Ranking Approach using Geospatial Techniques for Flash Flood Susceptibility Modelling in District Shangla, Pakistan

Every year, disaster strikes, and led to thousands of casualties and deaths around the world. A meteorological disaster such as a flash flood is a multifaceted hydro-meteorological phenomenon that can cause a huge loss of human life and can create severe economic problems. In this study, techniques based on Geographic information systems and Remote sensing were used to get the flood susceptibility map for District Shangla, Pakistan. For the susceptibility of flash floods, geo morphometric ranking model was used. Various causative factors were considered including; topography, river pattern, and flow accumulation. ALOS PALSAR digital elevation model was used for calculating the required causative factors. Eleven different sub-basins were delineated in the Shangla basin. A total of eighteen morphometric parameters were studied. The morphometric ranking approach (MRA) score was determined with a range of 1 to 5. Rank 5 represents high risk while rank 1 exhibits low risk. The results of the model were categorized into five flood vulnerability classes; very low, low, moderate, high and very high. The total population of Shangla district is 757,810 with a population density of 480 persons per sq km2, and results from this study revealed that 23 % of the total geographic area (364.11 km2) of the district is vulnerable to high flash floods.

Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches

The Karnali River Basin (KRB) comprises the longest river in Nepal, located south of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies. Proper floodplain management is essential to reduce the impacts due to rising flood frequency, magnitude, and severity aggravated by climate change. This research applies three machine learning techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), to flood event data from the KRB. Ten flood conditioning factors; Aspect, curvature, distance to a river (DTR), normalized difference vegetation index (NDVI), elevation, slope, rainfall, soil, stream power index (SPI), and topographical wetness index (TWI) were selected based on the multicollinearity test. The parameter performance was evaluated using the Cohen Kappa Score, with NDVI having the greatest influence, followed by elevation, DTR, curvature, and TWI. Based on the Area Under the Curve of Receiver Operating Characteristics (AUROC), SVM outperformed RF, ANN, and ANN for FSM. The area of very high flood susceptible areas ranges from 0.8 to 2.5% of the basin area, most of them located in the south with low slopes and elevations. The results of this study suggest the use of SVM for FSM to help with proper floodplain management.