Data-driven rapid flood prediction mapping with catchment generalizability

Abstract

Data-driven and machine learning models have recently received increasing interest to resolve the computational speed challenge faced by various physically-based simulations. A few studies have explored the application of these models to develop new, and fast, applications for fluvial and pluvial flood prediction, extent mapping, and flood susceptibility assessment. However, most studies have focused on model development for specific catchment areas, drainage networks or gauge stations. Hence, their results cannot be directly reused to other contexts unless extra data are available and the models are further trained. This study explores the generalizability potential of convolutional neural networks (CNNs) as flood prediction models. The study proposes a CNN-based model that can be reused in different catchment areas with different topography once the model is trained. The study investigates two options, patch- and resizing-based options, to process catchment areas of different sizes and different shapes. The results showed that the CNN-based model predicts accurately on “unseen” catchment areas with significantly less computational time when compared to physically-based models. The obtained results also suggest that the patch-based option is more effective than the resizing-based option in terms of prediction accuracy. In addition, all experiments have shown that the prediction of flow velocity is more accurate than water depth, suggesting that the water accumulation is more sensitive to global elevation information than flow velocity.