FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE

19 citations as recorded by crossref.

1. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm J. Debnath et al. https://doi.org/10.1007/s10661-023-12240-3
2. Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset J. V. R et al. https://doi.org/10.1002/for.70096
3. Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran M. Bashirgonbad et al. https://doi.org/10.1007/s11356-024-34501-5
4. Optimizing urban critical green space development using machine learning M. Ganjirad et al. https://doi.org/10.1016/j.scs.2025.106158
5. Impact of soil erosion on agricultural sustainability based on crop water productivity in semi-arid Iran E. Rafiei-Sardooi et al. https://doi.org/10.1038/s41598-025-24353-5
6. Geoinformatics and AHP multi criteria decision making integrated flood hazard zone mapping over Modjo catchment, Awash river basin, central Ethiopia B. Bedada & W. Dibaba https://doi.org/10.1007/s42452-025-06647-5
7. Uncertainty-Aware Flood Inundation Mapping With a Bayesian Deep Learning Framework Using SAR Imagery M. Garshasbi et al. https://doi.org/10.1109/JSTARS.2025.3610403
8. Machine learning-based optimization of flood susceptibility mapping in semi-arid zone H. Ait Naceur et al. https://doi.org/10.30493/das.2024.483211
9. From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning N. Hoang et al. https://doi.org/10.3390/s25041169
10. A comparative assessment of data-driven flood susceptibility mapping in Nigeria W. Montien Tique et al. https://doi.org/10.1007/s11069-025-07868-y
11. Flood risk mapping of the flood-prone Rangpur division of Bangladesh using remote sensing and multi-criteria analysis S. Rana et al. https://doi.org/10.1016/j.nhres.2023.09.012
12. 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. https://doi.org/10.1007/s44397-025-00012-2
13. Coğrafi Bilgi Sistemi Tabanlı Analitik Hiyerarşi Yöntemi ile Taşkın Duyarlılık Analizi: Oğulpaşa Deresi Havzası Örneği (Edirne) D. Bitek & R. Erenoğlu https://doi.org/10.46453/jader.1892689
14. Coastal urban flood risk management: Challenges and opportunities − A systematic review F. Aziz et al. https://doi.org/10.1016/j.jhydrol.2024.132271
15. An interpretable machine learning framework for mapping hotspots and identifying their driving factors in urban environments during heat waves N. Hoang et al. https://doi.org/10.1007/s10661-025-14461-0
16. Spatio-temporal analysis of slum growth and flood inundation in east african cities using machine learning K. Gilbert & Q. Shi https://doi.org/10.1007/s10708-026-11579-7
17. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. https://doi.org/10.3390/rs16040656
18. SemiCD-BG: disaster detection and anomaly intrusion detection in railroad operation using change detection G. Wen et al. https://doi.org/10.1080/29941849.2025.2572569
19. Innovative hybrid models combining CART and meta-heuristic algorithms for flood susceptibility mapping B. Mishra et al. https://doi.org/10.1080/09715010.2026.2666872