FLOOD SUSCEPTIBILITY MAPPING AND ASSESSMENT USING REGULARIZED RANDOM FOREST AND NAÏVE BAYES ALGORITHMS

13 citations as recorded by crossref.

1. Aggregated flood susceptibility mapping in Upper Chao Phraya River Basin using Shannon’s Entropy, Machine Learning, and Stacking ensemble methods G. Long et al. https://doi.org/10.1080/19475705.2026.2614731
2. Enhancing flood susceptibility mapping through integration of nature-inspired metaheuristic algorithms and machine learning M. Hassanvand et al. https://doi.org/10.1016/j.envc.2026.101479
3. GeoRisk Intelligence: Hybrid Ensemble Data-Driven Models with Recursive Feature Elimination for Landslide Susceptibility and Infrastructure Vulnerability in Uttarakhand A. Khouzani et al. https://doi.org/10.1007/s41748-025-00887-6
4. 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
5. From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland K. Gulshad et al. https://doi.org/10.3390/rs16203902
6. Geospatial analysis and machine learning for flood susceptibility mapping N. Bhattarai et al. https://doi.org/10.2166/wcc.2026.315
7. 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
8. Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of sub-tropical environment A. Ghosh et al. https://doi.org/10.1080/10106049.2023.2285355
9. Coastal Road Resilience in Nile Delta, Egypt: A Comprehensive Multihazard Risk Assessment D. Gamal Sahlol et al. https://doi.org/10.1061/NHREFO.NHENG-2470
10. An integrated approach for urban flood risk prediction using AHP-TOPSIS model: a case study of Jaipur region P. Deo et al. https://doi.org/10.1007/s11069-024-06965-8
11. Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya A. Ahmad et al. https://doi.org/10.1080/19475705.2025.2469766
12. Unveiling the performance of pre-processing approaches in machine learning based flood susceptibility mapping N. Gülcan & Ö. Ekmekcioğlu https://doi.org/10.1007/s11069-026-08034-8
13. Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms A. Islam et al. https://doi.org/10.1080/19475705.2025.2464049