Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.

中文翻译：

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使用多层感知器和自动编码器神经网络的新型深度学习模型对洪水敏感性进行映射和评估

洪水是全球最具破坏力的自然灾害之一，每年造成经济损失和人员伤亡。最近，研究人员广泛地将数据驱动技术与遥感（RS）和地理信息系统（GIS）相结合，用于洪水敏感性制图。这项研究提出了一种新颖的混合模型，该模型结合了多层感知器（MLP）和自动编码器模型，以生成位于伊朗和印度的两个研究区域的磁化率图。对于两种情况，分别将九个因素和十二个因素作为洪水敏感性制图的预测变量。通过接收器工作特性（AUROC）标准下的面积，将所提出的混合模型的预测能力与传统的MLP模型的预测能力进行了比较。对于伊朗和印度，MLP和自动编码器-MLP模型的AUROC曲线在训练阶段分别为75％和90％，74％和93％，在测试阶段分别为60％和91％，81％和97％。结果表明，混合自动编码器-MLP模型优于MLP模型，因此可以在洪水敏感性地图的其他研究中用作强大的模型。