Review of Flash Flood Susceptibility Modeling Derived from Machine Learning Algorithms with Input Data from Remote Sensing Sources
Author:
Affiliation:
1. Dept. of Civil and Environmental Engineering and Construction, Univ. of Nevada, Las Vegas, Las Vegas, NV.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784484852.109
Reference28 articles.
1. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees;Abedi R.;Geocarto International,2021
2. Random forest and naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt;Ahmed S.;Arabian Journal of Geosciences,2022
3. Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
4. Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran
5. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms;Band S. S.;Remote Sensing,2020
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