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Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNET_C) and recurrent neural network (NNET_R) models for flood hazard mapping. A flood-inundation inventory (including 295 flooded sites) was used as the response variable and 10 flood-affecting factors were considered as the predictor variables. Flooded sites were then spatially randomly split in a 70:30 ratio for building flood models and for validation purposes. The prediction quality of the models was validated using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The validation results indicated that prediction …