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

6 citations as recorded by crossref.

1. Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of sub-tropical environment A. Ghosh et al. 10.1080/10106049.2023.2285355
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. An integrated approach for urban flood risk prediction using AHP-TOPSIS model: a case study of Jaipur region P. Deo et al. 10.1007/s11069-024-06965-8
4. Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya A. Ahmad et al. 10.1080/19475705.2025.2469766
5. From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland K. Gulshad et al. 10.3390/rs16203902
6. Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms A. Islam et al. 10.1080/19475705.2025.2464049