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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.

Design and development of a practical land use change (LUC) model require both of a high prediction accuracy, to predict the future changes, and a well-fitted model reflecting and monitoring real-world. In this regard, many models follow the three phases of training, testing and validating in the modelling process to maximise both the accuracy and fitness. Therefore, the choice of model for different application is still a valid and important question. This paper applies and compares three widely-used data mining models of Classification And Regression Tree (CART), Multivariate Adaptive Regression Spline (MARS), and Random Forest (RF) to simulate urban LUCs of Shirgah in Iran. The results of these three phases for the three models of CART, MARS, and RF for the study area of Shirgah, in the North of Iran, verify that having the highest accuracy in the testing run does not necessarily guarantee the highest accuracy in the validating run. And so, with respect to the purpose of each project, such as modelling the current situation or predicting the future, the best model with the highest accuracy at the relevant phase or a combination of some/all should be selected. For example, in this study, MARS can provide with the best accuracy in validation run while with the lowest level of accuracy in the testing run. RF provides with the highest accuracy in testing run and the lowest level of accuracy in the validation run.

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