Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

• Published in: Journal of hydrology (Amsterdam) 2016-09, Vol.540, p.317-330
• Main Authors: Tien Bui, Dieu, Pradhan, Biswajeet, Nampak, Haleh, Bui, Quang-Thanh, Tran, Quynh-An, Nguyen, Quoc-Phi
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Flood susceptibility mapping; Floods; Fuzzy logic; Genetic evolutionary; GIS; Heuristic methods; Inference; Mathematical models; Neural fuzzy; Neural networks; Particle Swarm Optimization; Vietnam
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2016.06.027

•MONF is proposed for flood susceptibility modeling.•MONF has high performance on the training and validation datasets.•MONF outperforms benchmark models i.e. J48DT, RF, MLP Neural Nets, and SVM. This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE=0.306, MAE=0.094, AUC=0.962) and validation dataset (RMSE=0.362, MAE=0.130, AUC=0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.