Flood Susceptibility Mapping Using Image‐Based 2D‐CNN Deep Learning: Overview and Case Study Application Using Multiparametric Spatial Data in Data‐Scarce Urban Environments

This study presents results for urban flood susceptibility mapping (FSM) using image‐based 2D‐convolutional neural networks (2D‐CNN). The model input multiparametric spatial data comprised of land‐useland‐cover (LULC), digital elevation model (DEM), and the topographic and hydrologic conditioning derivatives, precipitation, and soil types. The implemented dropout regularization 2D‐CNN with ReLU activation function, categorical cross‐entropy loss function, and AdaGrad optimizer produced the case study area FSM with overall accuracy (OA) of 82.5%. The image‐based 2D‐CNN outperformed the multilayer perceptron (MLP) neural network by 18.4% in terms of overall accuracy and with corresponding lower MAE and higher F 1‐measures of 10.9% and 0.989, as compared to 25.6% and 0.877, respectively, for MLP‐ANN results. The accuracy of the 2D‐CNN that produced FSM map and the model efficiency were evaluated using area under the ROC curve (AUC) with respective success and prediction rates of 0.827 and 0.809. Using image‐based 2D‐CNN, 27% of the 247.7 km 2 of the studied area was mapped with a high risk of flooding, with MLP‐ANN overestimating the degree of high flood risk by 4.7%. Based on the gain ratio index analysis of the flood conditioning factors (FCFs), the most significant FCFs were LULC (18.5%), precipitation (14.9%), proximity to river (13.3%), and elevation (12.4%). Soil types contributed 8.6%, slope 9.1%, and the DEM‐derived hodological conditioning indicators contributed 23.2%. The study results demonstrate that in urban areas with scarce hydrological monitoring networks, the use of image‐based 2D‐CNN with multiparametric spatial data can produce high‐quality flood susceptibility maps for flood management in urban environments.