FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE

10 citations as recorded by crossref.

1. Flood risk mapping of the flood-prone Rangpur division of Bangladesh using remote sensing and multi-criteria analysis S. Rana et al. 10.1016/j.nhres.2023.09.012
2. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm J. Debnath et al. 10.1007/s10661-023-12240-3
3. Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran M. Bashirgonbad et al. 10.1007/s11356-024-34501-5
4. Coastal urban flood risk management: Challenges and opportunities − A systematic review F. Aziz et al. 10.1016/j.jhydrol.2024.132271
5. Optimizing Urban Critical Green Space Development Using Machine Learning M. Ganjirad et al. 10.1016/j.scs.2025.106158
6. Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri 10.1016/j.ecoinf.2024.102498
7. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
8. Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis P. Sibandze et al. 10.1007/s00267-024-02059-0
9. Prediction of flood susceptibility in an inter-fluvial region of Northern India using machine learning algorithms A. Ghosh & A. Siddiqui 10.1016/j.nhres.2024.12.006
10. Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan M. Tayyab et al. 10.1016/j.jenvman.2024.123094