Spatiotemporal Flood Prediction From Single Frame Input With a Post‐Processing Method

Abstract

In this study, a novel spatiotemporal hydrodynamic prediction task framework, named single frame prediction, was developed. The framework could generate results based on boundary conditions and a single flood map from the last time step, relying on hydrodynamic principles rather than historical trends, and doesn't require the assistance of traditional hydrodynamic models. Moreover, a post‐processing method based on physical laws was developed to refine the outputs of deep learning models at each time step, aiming to reduce accumulated errors in long‐term predictions. The performance of a widely used convolutional neural network‐based model, U‐Net, was evaluated to assess the feasibility of single frame prediction and the impact of the proposed post‐processing method. The experiments showed that single frame prediction could produce accurate flood maps, demonstrating the feasibility of the novel framework. Furthermore, the results indicated that the physics‐based post‐processing method could mitigate errors at each step, thereby enhancing prediction accuracy across entire flood event, showing strong effectiveness and applicability in flood prediction. Additionally, an ablation experiment was conducted to assess the effectiveness of each step in the method. The single frame prediction provided a more comprehensive and interpretable depiction of flood prediction processes with essential hydrodynamic variables, including water depth and unit discharge on all grid cells. The post‐processing method significantly reduced the accumulated error in the later stages of single frame prediction to an acceptable range with an average root‐mean‐square error of 0.041 m for water depth and 0.003 m²/s for unit discharge, suggesting a new technique for long‐term flood predictions.