A novel hybrid artificial intelligence approach for flood susceptibility assessment. (September 2017)

• Record Type: Journal Article
• Authors: Chapi, Kamran; Singh, Vijay P.; Shirzadi, Ataollah; Shahabi, Himan; Bui, Dieu Tien; Pham, Binh Thai; Khosravi, Khabat
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 229
• Page End: 245
• Publication Date: 2017-09
• Subjects: Flood susceptibility -- Bagging-LMT -- Bayesian logistic regression -- Logistic model tree -- Iran; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A new approach called Bagging-LMT is introduced for prediction of flood locations. Information Gain Ratio was conducted to determine affecting factors on Flood. Flood susceptibility map was prepared with the Bagging-LMT model. The map was compared with four previously-evaluated machine learning methods. Bagging-LMT properly predicted flood location and can be used as a management tool.