An assessment of metaheuristic approaches for flood assessment

Abstract

This study is performed to determine the importance of metaheuristic approaches and their accuracy with respect to the spatial modeling process emphasising on flood phenomena. In this study, four ensemble metaheuristic approaches, including the combinations of ANFIS (Adaptive Neuro-Fuzzy Inference System) with the genetic algorithm (GA), simulated annealing (SA), imperialist competitive algorithm (ICA), and differential evolution (DE), are used for flood zoning in Dingnan County, China. Based on historical records, aerial photo interpretation, field surveys, and Google Earth, the location of 115 floods are recorded in the study area. Different categorical and continuous factors affecting floods, including the plan curvature, altitude, distance to rivers, slope degree, rainfall, land use, lithology, stream power index, topographic wetness index, soil type, aspect, normalized difference vegetation index, and profile curvature, are identified in the study area and entered in GIS software. Flood susceptibility maps (FSMs) are validated using the ROC curve. The results confirm that the AUCs of the four combined metaheuristic models are larger than 79%. The highest AUC value is obtained for the ANFIS–GA ensemble (0.903), followed by the ANFIS–SA (0.843), ANFIS–DE (0.812), and ANFIS–ICA (0.798). The RMSEs obtained for the training data of the different models are 0.2562 (ANFIS-DE), 0.3121 (ANFIS-GA), 0.3345 (ANFIS-ICA), and 0.3398 (ANFIS-SA). The results of this study show that the proposed ensemble approaches are useful for flood hazard management and land use planning in Dingnan County, China, and other places.