Improving streamflow prediction in the WRF-Hydro model with LSTM networks

Kyeungwoo Cho, Yeonjoo Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Researchers have attempted to use machine learning algorithms to replace physically based models for streamflow prediction. Although existing studies have contributed to improving machine learning methods, they still have weaknesses, such as large dataset requirements and overfitting. Therefore, we propose an approach that combines the Weather Research and Forecasting hydrological modeling system (WRF-Hydro) and the Long Short-Term Memory (LSTM) network, i.e., WRF-Hydro-LSTM, to improve streamflow simulations. In this approach, LSTM was employed to predict the residual errors of WRF-Hydro; in contrast, the conventional approach with LSTM predicts streamflow directly. Here, we performed numerical experiments to predict the inflow of Soyangho Lake in South Korea using WRF-Hydro-LSTM, WRF-Hydro-only, and LSTM-only. WRF-Hydro-LSTM and LSTM-only showed better results (NSE = 0.95 and R greater than 0.96) compared to WRF-Hydro-only (NSE = 0.72 and R = 0.88); however, in terms of the percent bias, WRF-Hydro-LSTM had a better value (1.75) than LSTM-only (17.36). While the LSTM-only follows objective functions and not physical principles, WRF-Hydro-LSTM simulates residual errors and efficiently decreases uncertainties that are inherent with conventional methods. Furthermore, a sensitivity test on the training dataset indicated that the correlation coefficient and NSE value were not overly sensitive, but the PBIAS value differed substantially depending on the training set. This study demonstrates that WRF-Hydro-LSTM is particularly useful for representing real-world physical constraints and thus can potentially improve streamflow prediction compared to using either of the two approaches exclusively.

Original languageEnglish
Article number127297
JournalJournal of Hydrology
Volume605
DOIs
Publication statusPublished - 2022 Feb

Bibliographical note

Funding Information:
This work was based on a Master’s thesis by Kyeungwoo Cho at Yonsei University (February 2020), advised by Yeonjoo Kim. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT ( 2018R1A1A3A04079419 and 2020R1A2C2007670 ), and by the Technology Advancement Research Program through the Korea Agency for Infrastructure Technology Advancement (KAIA) funded by the Ministry of Land, Infrastructure, and Transport ( 21CTAP-C163541-01 ).

Funding Information:
This work was based on a Master's thesis by Kyeungwoo Cho at Yonsei University (February 2020), advised by Yeonjoo Kim. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (2018R1A1A3A04079419 and 2020R1A2C2007670), and by the Technology Advancement Research Program through the Korea Agency for Infrastructure Technology Advancement (KAIA) funded by the Ministry of Land, Infrastructure, and Transport (21CTAP-C163541-01).

Publisher Copyright:
© 2021 The Author(s)

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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