SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks
Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models-LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble...
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| Published in | Geomatics, natural hazards and risk Vol. 15; no. 1 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
31.12.2024
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models-LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble-developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble's superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks. |
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| AbstractList | Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks. |
| Author | Ewert, Frank Halder, Krishnagopal Bisai, Dipak Islam, Abu Reza Md. Towfiqul Chatterjee, Uday Alam, Edris Ghosh, Anitabha Srivastava, Amit Kumar Pal, Subodh Chandra Gaiser, Thomas Islam, Md Kamrul |
| Author_xml | – sequence: 1 givenname: Krishnagopal surname: Halder fullname: Halder, Krishnagopal organization: Centre of Excellence in Disaster Mitigation and Management (CoEDMM), Indian Institute of Technology Roorkee – sequence: 2 givenname: Anitabha surname: Ghosh fullname: Ghosh, Anitabha organization: Coastal Environmental Studies Research Centre, Egra S.S.B. College, (Affiliated to Vidyasagar University) – sequence: 3 givenname: Amit Kumar surname: Srivastava fullname: Srivastava, Amit Kumar organization: Leibniz Centre for Agricultural Landscape Research (ZALF) – sequence: 4 givenname: Subodh Chandra surname: Pal fullname: Pal, Subodh Chandra organization: Department of Geography, The University of Burdwan – sequence: 5 givenname: Uday surname: Chatterjee fullname: Chatterjee, Uday organization: Department of Geography, Bhatter College – sequence: 6 givenname: Dipak surname: Bisai fullname: Bisai, Dipak organization: Coastal Environmental Studies Research Centre, Egra S.S.B. College, (Affiliated to Vidyasagar University) – sequence: 7 givenname: Frank surname: Ewert fullname: Ewert, Frank organization: Leibniz Centre for Agricultural Landscape Research (ZALF) – sequence: 8 givenname: Thomas surname: Gaiser fullname: Gaiser, Thomas organization: Institute of Crop Science and Resource Conservation, University of Bonn – sequence: 9 givenname: Abu Reza Md. Towfiqul surname: Islam fullname: Islam, Abu Reza Md. Towfiqul organization: Department of Development Studies, Daffodil International University – sequence: 10 givenname: Edris surname: Alam fullname: Alam, Edris organization: Department of Geography and Environmental Studies, University of Chittagong – sequence: 11 givenname: Md Kamrul surname: Islam fullname: Islam, Md Kamrul organization: Department of Civil and Environmental Engineering College of Engineering, King Faisal University |
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