Flood is one of the most destructive natural disasters globally and is a concern due to its high vulnerability. In this study for identification of flood susceptible areas, artificial neural network (ANN) and Multi-Attributive Border Approximation Area Comparison (MABAC) combined with Weights of Evidence (WoE) and Analytical Hierarchy Process (AHP) Models were used in Mazandaran province, Iran. MABAC method was used for the first time to evaluate the flood-prone areas in this study, and Attempts have been made for evaluate the performance of this new method by comparing with ANN model. The output of the neural network was discharge values in hydrometric stations. Using Geographic Information System (GIS) with eight effective factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, a flood model developed. Three precision parameters containing \({R}^{2}\), RMSE and MAE were applied to show the performance of the ANN model which yielded the values of 0.89, 0.0024 \({m}^{3}/s\), and 0.0018 \({m}^{3}/s\), respectively for testing data. The verification results indicated satisfactory agreement between the predicted and the real hydrological records. Also, based on flood inventory map and using the area under receiver operating curve, predictive power of the MABAC-WoE-AHP model was evaluated. The AUC value for prediction rate of this model was 86.1% which indicates the very good accuracy in predicting flood-prone areas. Comparison of flood susceptibility maps for ANN and MABAC-WoE-AHP models showed the good agreement between two models, that clarifies the efficiency of the new proposed method for future preventive measures.

洪水是全球最具破坏力的自然灾害之一，由于其高度脆弱性，因此备受关注。在这项研究中，为了识别洪水易感区域，在伊朗马赞达兰省使用了人工神经网络（ANN）和多属性边界近似区域比较（MABAC）结合证据权重（WoE）和层次分析过程（AHP）模型。这项研究首次使用MABAC方法来评估易发洪水地区，并已尝试通过与ANN模型进行比较来评估该新方法的性能。神经网络的输出是水文测量站的流量值。使用地理信息系统（GIS），它具有八种有效因素，包括降雨，与河流的距离，坡度，土壤，地质，海拔，排水密度和土地利用，开发了洪水模型。包含三个精度参数\（{R} ^ {2} \），RMSE和MAE用于显示ANN模型的性能，该模型得出的值分别为0.89、0.0024 \（{m} ^ {3} / s \）和0.0018 \ （{m} ^ {3} / s \），分别用于测试数据。验证结果表明，预测的和实际的水文记录之间的令人满意的一致性。此外，根据洪水清单图并使用接收器工作曲线下的面积，评估了MABAC-WoE-AHP模型的预测能力。该模型的预测率的AUC值为86.1％，表明在预测易发洪灾地区的准确性非常好。ANN模型和MABAC-WoE-AHP模型的洪水敏感性图的比较表明，这两个模型之间具有良好的一致性，这说明了所提出的新方法对未来预防措施的有效性。