Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting

Document Type

Article

Publication Date

6-2025

Department

Department of Computer Science

Abstract

Urban real-time rainfall-runoff forecasting (URRF) offers an economical and efficient approach to assessing flood risks in urban areas. However, the hydrological processes of urban rainfall are characterized by high nonlinearity and long-term dependencies due to strong uncertainties and significant human influences, making URRF a challenging task in hydrological simulation. Existing methods often fall short in meeting the requirements for real-time response and accuracy. To address these limitations, this paper proposes a novel global-encoder and local-decoder (GL-ED) model. The global encoder extracts global temporal features, while the local decoder focuses on forecasting. A temporal fully connected (TFC) module is introduced within the global encoder to capture the global features of runoff sequences, overcoming the limitation of convolutional operations that primarily focus on local information. Additionally, to tackle the uneven distribution of urban rainfall-runoff data, a novel RD loss function is proposed, combining dynamic time warping (DTW) with RMSE to better guide the training of complex features. The GL-ED model was evaluated using observed urban rainfall events from January 2018 to December 2019 in a 3.52 km2 complex terrain area in Chongqing, China. Experimental results demonstrate that the GL-ED model outperforms conventional deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, in terms of NSE, MAE, and RMSE. In ablation experiments, the GL-ED model trained with the RD loss function achieved an average improvement of 2.51% in NSE and 5.62% in KGE, while reducing RMSE, MAE, and Pbias by 6.08%, 11.31%, and 93%, respectively. These findings highlight the model’s capability to provide reliable and accurate real-time rainfall-runoff forecasting, offering significant potential for enhancing urban flood risk management and decision-making processes.

Publication Title

Earth Science Informatics

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