SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks

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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Bibliographic Details
Main Authors: Krishnagopal Halder, Anitabha Ghosh, Amit Kumar Srivastava, Subodh Chandra Pal, Uday Chatterjee, Dipak Bisai, Frank Ewert, Thomas Gaiser, Abu Reza Md. Towfiqul Islam, Edris Alam, Md Kamrul Islam
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Disaster management
Flood susceptibility
Google Earth Engine
Machine learning
Python
Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2024.2409202

Internet

https://www.tandfonline.com/doi/10.1080/19475705.2024.2409202

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