Dual-modality Encoder-decoder Framework for Urban Real-time Rainfall-runoff Prediction

Urban real-time rainfall-runoff prediction (URRP) is a potential nonstructural measure for urban flood management. Due to significant influence of human activities and the built environment, it is still a challenging issue to obtain satisfactory results that support effective emergency response. To obtain accurate and stable prediction results, this investigation employs rainfall and runoff datasets that mutually complementary to construct URRP model, providing more comprehensive features of runoff process. To extract features from such two kinds of datasets, a dual-modality encoder-decoder (DM-ED) model is proposed. DM-ED model employs encoder-decoder (ED) framework to enhance multi-steps prediction performance, and LSTM is embedded in the encoder and decoder layers, respectively, thus capturing high time-dependence and non-linearity of rainfall and runoff features. Then interactive dual cross (IDC) attention module is designed to capture global cross feature between rainfall and runoff. Additionally, different from common used pre-fusion approach, we propose a post-fusion (PF) module to efficiently fuse rainfall and runoff features, which can capture more comprehensive information and enhance model robustness. The DM-ED with IDC and PF model is trained and tested on urban rainfall-runoff events (January 2018 - December 2019) over a 3.52 $$km^2$$ terrain in Chongqing, China. Several experiments have been conducted on this terrain, and the experimental results show that NSE, RMSE, $$R^2$$ , MAE coefficients outperform other traditional models. The results indicate that DM-ED with IDC and PF model is expected to offer a reliable and effective method for URRP tasks.