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

Yuan Tian, Ruonan Cui (), Wenlong Fu, Zhenyu Ao, Qi Han, Yi Xiang and Xinyu Lei
Additional contact information
Yuan Tian: Chongqing University of Science and Technology
Ruonan Cui: Chongqing University of Science and Technology
Wenlong Fu: Chongqing University of Science and Technology
Zhenyu Ao: Chongqing University of Science and Technology
Qi Han: Chongqing University of Science and Technology
Yi Xiang: Chongqing University of Science and Technology
Xinyu Lei: Michigan Technological University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 9, No 14, 4509-4538

Abstract: Abstract 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.

Keywords: Urban real-time rainfall-runoff prediction; Dual-modality encoder-decoder; LSTM; Interactive dual cross attention; Post-fusion (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04166-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04166-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04166-x

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().