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

14 citations as recorded by crossref.

1. Classification of flood-prone areas in Mexico: a model using Naive Bayes B. Sandoval-A et al. https://doi.org/10.1080/28375807.2026.2725906
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4. 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
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6. From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland K. Gulshad et al. https://doi.org/10.3390/rs16203902
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9. 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
10. 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
11. 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
12. 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
13. 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
14. 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