An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

• Published in: The Science of the total environment Vol. 651; no. Pt 2; pp. 2087 - 2096
• Main Authors: Choubin, Bahram, Moradi, Ehsan, Golshan, Mohammad, Adamowski, Jan, Sajedi-Hosseini, Farzaneh, Mosavi, Amir
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.02.2019
• Subjects: altitude; case studies; Classification and regression trees; discriminant analysis; drainage; Ensemble approach; environmental impact; Flood susceptibility; floods; inventories; Iran; land use; Multivariate discriminant analysis; prediction; remediation; residential areas; rivers; soil depth; support vector machines; Support vector regression; water management; watersheds; Iran; Support vector regression; Ensemble approach; Classification and regression trees; Flood susceptibility; Multivariate discriminant analysis
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2018.10.064

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods. [Display omitted] •Ensemble machine learning (ML) predicting flood susceptibility•Contribution of models with accuracy values above 80% in ensembling process•Area under curve (AUC) for the models ranges from 0.83 to 0.89.•ML allows quick priority of prone areas for the remediation of floods.