The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
Abstract
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short‐term memory (LSTM) model and sequence‐to‐sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long‐term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall‐runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24‐hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash‐Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root‐mean‐square error. The results show that the LSTM‐seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.
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Cited by
Tripathy, K. P., & Mishra, A. K. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130458 · Cited by 336
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Flood forecasting with machine learning models in an operational framework
Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., et al. (2022) · Hydrology and Earth System Sciences · doi:10.5194/hess-26-4013-2022 · Cited by 267
Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr, A., & Kwon, H.-H. (2021) · Water Resources Management · doi:10.1007/s11269-021-02937-w · Cited by 266
Feng, D., Lawson, K., & Shen, C. (2021) · Geophysical Research Letters · doi:10.1029/2021gl092999 · Cited by 162
Metadata
- Authors
- Xiang, Z., Yan, J., & Demir, I.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2020-01
- Volume
- 56
- Issue
- 1
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W2998268303
- Cited by
- 768
- References
- 44
- FWCI
- 30.88
- Type
- journal article
- Language
- en
- Open access
- closed
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