Assessment of flood susceptibility prediction based on optimized tree-based machine learning models. Issue 6 (3rd June 2022)

• Record Type: Journal Article
• Authors: Eslaminezhad, Seyed Ahmad; Eftekhari, Mobin; Azma, Aliasghar; Kiyanfar, Ramin; Akbari, Mohammad
• Is Part Of: Journal of water and climate change. Volume 13:Issue 6(2022)
• Page Start: 2353
• Page End: 2385
• Publication Date: 2022-06-03
• Subjects: flood susceptibility prediction -- Iran -- machine learning -- optimization; Water -- Periodicals; Hydrology -- Periodicals; Climatic changes -- Periodicals; Climatic changes; Hydrology; Water; Electronic journals; Periodicals; 333.9116
• DOI: 10.2166/wcc.2022.435
• Languages: English
• ISSNs: 2040-2244

Abstract

Abstract: Due to the physical processes of floods, the use of data-driven machine learning (ML) models is a cost-efficient approach to flood modeling. The innovation of the current study revolves around the development of tree-based ML models, including Rotation Forest (ROF), Alternating Decision Tree (ADTree), and Random Forest (RF) via binary particle swarm optimization (BPSO), to estimate flood susceptibility in the Maneh and Samalqan watershed, Iran. Therefore, to implement the models, 370 flood-prone locations in the case study were identified (2016–2019). In addition, 20 hydrogeological, topographical, geological, and environmental criteria affecting flood occurrence in the study area were extracted to predict flood susceptibility. The area under the curve (AUC) and a variety of other statistical indicators were used to evaluate the performances of the models. The results showed that the RF-BPSO (AUC=0.935) has the highest accuracy compared to ROF-BPSO (AUC=0.904), and ADTree-BPSO (AUC=0.923). In addition, the findings illustrated that the chance of flooding in the center of the area in question is greater than in other points due to lower elevation, lower slope, and proximity to rivers. Therefore, the ensemble framework proposed here can also be used to predict flood susceptibility maps in other regions with similar geo-environmental characteristics for flood management and prevention. HIGHLIGHTS: Comparative assessment of tree-based machine learning models to classify locations as either flooded or non-flooded. Development of machine learning models BPSO algorithm. A total of 20 geo-environmental criteria were used for flood susceptibility mapping. Determining flood-affecting criteria using the BPSO algorithm. Sensitivity analysis of 20 geo-environmental criteria in predicting flood susceptibility. Graphical Abstract