UAV-based DEM augmentation using ConSinGAN for efficient flood parameter prediction with machine learning and 1D hydrodynamic models

Abstract

Flooding disasters have a significant financial impact on metropolitan regions, highlighting the critical need for accurate flood prediction methods. This study addresses the problem of limited datasets in flood prediction by employing a novel approach that integrates Unmanned Aerial Vehicle (UAV)-based Digital Elevation Models (DEMs) and the ConSinGAN generative model to enhance flood prediction accuracy. The methodology involves synthesizing DEMs using ConSinGAN to augment small datasets, followed by the application of three machine learning (ML) algorithms—Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—to predict crucial flood-related parameters such as discharge and elevation. The predictive performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with units specified as meters for elevation and cubic meters per second for discharge. The RF model exhibited superior predictive capabilities, achieving a training RMSE of 0.68 cubic meters per second and an MAE of 0.30 cubic meters per second for discharge predictions. For elevation predictions, the RF model achieved a training RMSE of 0.0101 m and an MAE of 0.0046 m. Cross-validation results indicated the lowest RMSE values of 2.44 cubic meters per second for discharge and 0.03 m for elevation, with corresponding MAE values of 1.37 cubic meters per second and 0.02 m, respectively. This integrated approach, combining UAV-based DEM augmentation and advanced ML techniques, provides a robust tool for urban planners and decision-makers to accurately forecast flood parameters, thereby facilitating better disaster preparedness and mitigation strategies.