FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE

12 citations as recorded by crossref.

1. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm J. Debnath et al. 10.1007/s10661-023-12240-3
2. Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran M. Bashirgonbad et al. 10.1007/s11356-024-34501-5
3. Optimizing urban critical green space development using machine learning M. Ganjirad et al. 10.1016/j.scs.2025.106158
4. Geoinformatics and AHP multi criteria decision making integrated flood hazard zone mapping over Modjo catchment, Awash river basin, central Ethiopia B. Bedada & W. Dibaba 10.1007/s42452-025-06647-5
5. 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
6. From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning N. Hoang et al. 10.3390/s25041169
7. 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
8. Application of sentinel-1 SAR data for flood monitoring in the lower Ganges Basin: a time-series analysis of 2021 flood in Bihar M. Sajid et al. 10.1007/s44397-025-00012-2
9. Coastal urban flood risk management: Challenges and opportunities − A systematic review F. Aziz et al. 10.1016/j.jhydrol.2024.132271
10. An interpretable machine learning framework for mapping hotspots and identifying their driving factors in urban environments during heat waves N. Hoang et al. 10.1007/s10661-025-14461-0
11. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
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