Flood susceptibility mapping using image-based 2D-CNN deep learning: overview and case study application using multiparametric spatial data in data-scarce urban environments

Abstract

Tis study presents results for urban food susceptibility mapping (FSM) using image-based 2D-convolutional neural networks (2D-CNN). Te 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. Te 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%. Te 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 F1-measures of 10.9% and 0.989, as compared to 25.6% and 0.877, respectively, for MLP-ANN results. Te accuracy of the 2D-CNN that produced FSM map and the model efciency 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 km2 of the studied area was mapped with a high risk of fooding, with MLP-ANN overestimating the degree of high food risk by 4.7%. Based on the gain ratio index analysis of the food conditioning factors (FCFs), the most signifcant 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%. Te 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 food susceptibility maps for food management in urban environments.