SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks Article Swipe
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
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/19475705.2024.2409202
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/19475705.2024.2409202
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/19475705.2024.2409202
- https://www.tandfonline.com/doi/pdf/10.1080/19475705.2024.2409202?needAccess=true
- OA Status
- gold
- Cited By
- 8
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403459510
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403459510Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/19475705.2024.2409202Digital Object Identifier
- Title
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SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-16Full publication date if available
- Authors
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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 IslamList of authors in order
- Landing page
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https://doi.org/10.1080/19475705.2024.2409202Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/19475705.2024.2409202?needAccess=trueDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.tandfonline.com/doi/pdf/10.1080/19475705.2024.2409202?needAccess=trueDirect OA link when available
- Concepts
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Flood myth, Ensemble learning, Ensemble forecasting, Computer science, Artificial intelligence, Environmental science, Machine learning, Remote sensing, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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