Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

• Published in: Stochastic environmental research and risk assessment 2020-12, Vol.34 (12), p.2277-2300
• Main Authors: Talukdar, Swapan, Ghose, Bonosri, Shahfahad, Salam, Roquia, Mahato, Susanta, Pham, Quoc Bao, Linh, Nguyen Thi Thuy, Costache, Romulus, Avand, Mohammadtaghi
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Artificial intelligence; Bagging; Casualties; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Decision trees; Developing countries; Earth and Environmental Science; Earth Sciences; Environment; Flood control; Flood management; Flood predictions; Flooding; Floods; LDCs; Learning algorithms; Machine learning; Math. Appl. in Environmental Science; Modelling; Original Paper; Physics; Probability Theory and Stochastic Processes; Property damage; Rank tests; Remote sensing; Risk reduction; River basins; Rivers; Statistical analysis; Statistics for Engineering; Susceptibility; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-020-01862-5

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal–Wallis test and Kolmogorov–Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km 2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. 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 measures to avoid potential damages. Graphic abstract