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  • Presentation | H21U: Recent Advances in Large-Scale Hydrologic and Flood Modeling: Assessing and Predicting Extreme Floods III Poster
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  • H21U-1258: Spatiotemporal Assessment of Flood Susceptibility Using Remote Sensing Indices and Random Forest Classification in Sunamganj District, Bangladesh
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  • Board 1258‚ Hall EFG (Poster Hall)
    NOLA CC
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Author(s):
Md. Rakibul Hasan, National Oceanographic And Maritime Institute (NOAMI) (First Author, Presenting Author)
Sabyasachi Paul, Urban and Rural Planning Discipline, Khulna University
Fahmida Sultana, Urban and Rural Planning Discipline, Khulna University
Sadia Nasrin Shara, University of Dhaka
Sohanur Rahman Shuvo, Urban and Rural Planning Discipline, Khulna University
Hridoy Saha, University of Dhaka


This study focuses on identifying flood-prone areas in Sunamganj District, Bangladesh, using satellite-based remote sensing indices and the Random Forest machine learning algorithm. We analyzed NDVI, NDBI, NDWI, MNDWI, and SAVI from the years 2016, 2020, and 2024 to assess changes in vegetation, built-up areas, and water bodies. These indices were used as input features to train the Random Forest model, which effectively classified areas into different flood susceptibility zones. The results highlight both spatial and temporal shifts in flood risk and provide useful insights for flood management and planning.



Scientific Discipline
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