Flood susceptibility modelling using advanced ensemble machine learning models

By Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, K. Eibek, Quoc Bao Pham, Alban Kuriqi, Nguyen Thi Thuy Linh. 2020

483 citations recorded by the source corpus.

Abstract Because of the tremendous damage to properties, infrastructures, and human casualties, floods are one of the greatest devastating disasters from nature. Due to the dynamic and complex nature of the flash flood, it is challenging to predict the sites which are vulnerable to flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/eba2533af2ba7deacbff7237c0898be04360d04d