Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations

Natural Hazards, Год журнала: 2022, Номер 116(3), С. 2957 - 2991

Опубликована: Дек. 20, 2022

Язык: Английский

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Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

Remote Sensing, Год журнала: 2022, Номер 15(1), С. 192 - 192

Опубликована: Дек. 29, 2022

Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management reducing its harmful effects. In this study, new machine learning model based on Cascade Forest Model (CFM) was developed FSM. Satellite imagery, historical reports, field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. performance proposed CFM evaluated over two study areas, results compared with those other six methods, including Support Vector Machine (SVM), Decision Tree (DT), Random (RF), Deep Neural Network (DNN), Light Gradient Boosting (LightGBM), Extreme (XGBoost), Categorical (CatBoost). result showed produced highest accuracy models both Overall Accuracy (AC), Kappa Coefficient (KC), Area Under Receiver Operating Characteristic Curve (AUC) more than 95%, 0.8, 0.95, respectively. Most these recognized southwestern part Karun basin, northern northwestern regions Gorganrud basin as susceptible

Язык: Английский

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Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India

Urban Climate, Год журнала: 2023, Номер 49, С. 101503 - 101503

Опубликована: Март 18, 2023

Язык: Английский

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Wetland identification through remote sensing: Insights into wetness, greenness, turbidity, temperature, and changing landscapes

Big Data Research, Год журнала: 2023, Номер 35, С. 100416 - 100416

Опубликована: Ноя. 9, 2023

Язык: Английский

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Identification of time-varying wetlands neglected in Pakistan through remote sensing techniques

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(29), С. 74031 - 74044

Опубликована: Май 18, 2023

Язык: Английский

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Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India

Geomatics Natural Hazards and Risk, Год журнала: 2022, Номер 13(1), С. 2183 - 2226

Опубликована: Авг. 19, 2022

Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) Analytical Hierarchy Process (AHP) method for identifying flood susceptibility zonation (FSZ), vulnerability (FVZ), risk (FRZ) humid subtropical Uttar Dinajpur district India. study combined a large number thematic layers (N = 12 FSZ N 9 FVZ) achieve reliable accuracy included multicollinearity analysis these variables overcome issues related highly correlated variables. According findings, 27.04, 15.62, 4.59% area were classified as medium, high, very high FRZ, respectively. ROC-AUC, MAE, MSE, RMSE model exhibited good prediction 0.73, 0.15, 0.16, 0.21, performance AHP has been evaluated using sensitivity analyses. It also recommends that persistent improvement this subject, such studies modifying criteria thresholds, changing relative criteria, desired matrix, will permit GIS MCDA be progressively adapted real hazard-management issues.

Язык: Английский

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Flood susceptibility prediction using tree-based machine learning models in the GBA

Sustainable Cities and Society, Год журнала: 2023, Номер 97, С. 104744 - 104744

Опубликована: Июнь 25, 2023

Язык: Английский

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Flood susceptibility mapping using hybrid models optimized with Artificial Bee Colony

Journal of Hydrology, Год журнала: 2023, Номер 624, С. 129961 - 129961

Опубликована: Июль 19, 2023

Язык: Английский

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Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization

Applied Soft Computing, Год журнала: 2023, Номер 148, С. 110846 - 110846

Опубликована: Сен. 13, 2023

Язык: Английский

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

Water, Год журнала: 2024, Номер 16(1), С. 173 - 173

Опубликована: Янв. 3, 2024

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

Язык: Английский

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