Multi attributive ideal-real comparative analysis (MAIRCA) method for evaluating flood susceptibility in a temperate Mediterranean climate

Abstract

ABSTRACT This study is focused on flood susceptibility evaluation across the Golestan Province, Iran, using novel ensemble models generated by Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) with frequency ratio (FR) and weight of evidence (WOE). As MAIRCA was employed in flood susceptibility assessment for the first time, an attempt has been made to evaluate its capability by comparing the ensemble with multilayer perceptron (MLP) neural network-based models. Ten flood conditioning factors (altitude, slope, aspect, plan curvature, distance from rivers, topographic wetness index, rainfall, soil type, geology, and land use) and 240 flood and non-flood locations were applied for modelling, of which 70% were selected for training and 30% for validation. The results of validation, performed by the receiver operating characteristics curve method, indicate that the highest predictive accuracy was obtained by MLP-WOE (0.926), followed by MLP-FR (0.912), MAIRCA-WOE (0.885), and MAIRCA-FR (0.859). High precision of the models implies their capability in flood risk prediction.