Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts

Abstract

Reliable and accurate regional multistep-ahead flood forecasts during extreme events are crucial and beneficial to flood disaster management and preparedness. Hydrologic uncertainty associated with the nonlinear dependence structure of flood inundation dynamics makes flood inundation forecasting fundamentally challenging. This study proposes a novel machine learning-based model (SAE-RNN) that hybrids the stacked autoencoder (SAE) with a recurrent neural network (RNN) for providing accurate and timely information to support emergency management in areas impacted by flood hazards. The proposed SAE-RNN model uses SAE to compress (encode) the high-dimensional flood inundation depths in a wide region into a low-dimensional latent space representation (flood features), uses RNN to forecast multistep-ahead flood features based on regional rainfall patterns, and finally uses SAE to reconstruct (decode) the multistep-ahead forecasts of flood features into regional flood inundation depths. A large number of hourly datasets of flood inundation depths collected in Yilan County of Taiwan formed the case study, where each dataset contains 169,797 grids of inundation depth. The datasets were divided into three independent datasets for use in training, validating and testing stages. The models’ results showed that RMSE values were very small (<0.09 m) and R2 values were high (>0.95) in all the cases (1- up to 3-hour-ahead forecasts in three stages). We conclude that the reason why the proposed SAE-RNN models are capable of attaining favorable regional multistep-ahead flood inundation forecasts could be owing to two core strategies: the effective continual extraction of the nonlinear dependence structure from flood inundation dynamics for lessening hydrologic uncertainty by virtue of SAE; and the nonlinear conversion of rainfall sequences into future flood features by virtue of RNN.