Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan

• Published in: Geomatics, natural hazards and risk Vol. 15; no. 1
• Main Authors: Al-Sheriadeh, Mohanned S., Daqdouq, Mohammad A.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Artificial neural networks; Distance; Elevation; Flood mapping; flood susceptibility; Floods; Learning algorithms; Machine learning; Machine learning algorithms; Mapping; Mountain regions; Mountainous areas; Neural networks; Rainfall; Rainfall simulators; random forest; Rivers; support vector machine; Support vector machines; Susceptibility; Training; Watersheds; Wetness index
• ISSN: 1947-5705; 1947-5713; 1947-5713
• DOI: 10.1080/19475705.2024.2378991

The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountainous areas. Because of a shortage of past floods location, a physical model was utilized to generate them based on simulations of 100-year rainfall. 10,000 of them were selected randomly and used for MLAs training and testing. During training, thirteen flood influential factors were identified. Out of them, the distance to stream, elevation, and topographic wetness index have shown an overwhelming effect in Zarqa Ma'in watershed (they gained 50% of IGR), while the distance to stream, stream density, and elevation have an overwhelming effect in Al-Buaida watershed (they gained 44% of IGR). For flood susceptibility mapping, RF outperformed the other two algorithms for both watersheds and was thus selected for susceptibility mapping. The maps were classified into five classes, and 11% of Al-Buaida watershed fell into high to very high classes, while 5.2% of Zarqa Ma'in watershed fell within these classes. In conclusion, MLAs were able to produce susceptibility maps efficiently, and they can form an alternative to physical modeling.