Enhancement of real-time flood inundation prediction based on deep learning with dimensionality compression

Abstract

This paper presents a flood inundation prediction model that integrates deep learning with dimensionality compression as an alternative to conventional numerical simulation approaches. Three dimensionality compression techniques—Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Autoencoders (AE)—were evaluated in conjunction with various deep learning models, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The DNN-based model was applied to flood simulations of the Arakawa River to assess its feasibility and effectiveness in terms of both prediction accuracy and real-time performance. Given the temporal nature of inundation dynamics, time-series models such as RNN, LSTM, and GRU were employed to capture temporal dependencies. Results showed that high-accuracy and high-speed prediction is achievable even when the original 221,392-dimensional inundation data is compressed to a 10-dimensional representation. Among the tested methods, NMF exhibited the best balance between accuracy and computational efficiency. Furthermore, LSTM demonstrated superior prediction accuracy compared to DNN.