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A novel hybrid artificial intelligence approach for flood susceptibility assessment

Chapi, Kamran, Singh, Vijay P., Shirzadi, Ataollah, Shahabi, Himan, Bui, Dieu Tien, Pham, Binh Thai, Khosravi, Khabat
Environmental modelling & software 2017 v.95 pp. 229-245
artificial intelligence, computer software, databases, environmental models, graphs, inventories, logit analysis, watersheds, Iran
A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas.