Li, Bu;
Li, Ruidong;
Sun, Ting;
Gong, Aofan;
Tian, Fuqiang;
Yawar Ali Khan, Mohd;
Ni, Guangheng;
(2023)
Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: a case study of three mountainous areas on the Tibetan Plateau.
Journal of Hydrology
, 620
(A)
, Article 129401. 10.1016/j.jhydrol.2023.129401.
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Abstract
Long short-term memory (LSTM) networks have demonstrated their excellent capability in processing long-length temporal dynamics and have proven to be effective in precipitation-runoff modeling. However, the current LSTM hydrological models lack the incorporation of multi-task learning and spatial information, which limits their ability to make full use of meteorological and hydrological data. To address this issue, this study proposes a spatiotemporal deep-learning (DL)-based hydrological model that couples the 2-Dimension convolutional neural network (CNN) and LSTM and introduces actual evaporation ( ) as an additional training target. The proposed CNN-LSTM model is tested on three large mountainous basins on the Tibetan Plateau, and the results are compared to those obtained from the LSTM-only model. Additionally, a probe method is used to decipher the internal embedding layers of the proposed DL models. The results indicate that both LSTM and CNN-LSTM hydrological models perform well in simulating runoff ( ) and , with Nash-Sutcliffe efficiency coefficients ( ) higher than 0.82 and 0.95, respectively. The higher suggest that introducing spatial information into LSTM-only models can improve the overall and peak model performance. Moreover, multi-task simulation with LSTM-only models shows better accuracy in the estimation of volume and performance, with increasing by approximately 0.02. The probe method also reveals that CNN can capture the basin-averaged meteorological values in CNN-LSTM models, while LSTM ( ) models contain the information about the known ( ) process. Overall, this study demonstrates the value of spatial information and multi-task learning in LSTM hydrological modeling and provides a perspective for interpreting the internal embedding layers of DL models.
| Type: | Article |
|---|---|
| Title: | Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: a case study of three mountainous areas on the Tibetan Plateau |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jhydrol.2023.129401 |
| Publisher version: | https://doi.org/10.1016/j.jhydrol.2023.129401 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | CNN-LSTM, spatiotemporal, multi-task, actual evaporation, Tibetan Plateau |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10166707 |
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