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Open AccessArticle

Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

1
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
2
Risk Management Office, KB Claims Survey and Adjusting, Seoul 04027, Korea
3
Institute of Water Resources System, Inha University, Incheon 22212, Korea
4
Department of Civil Engineering, Inha University, Incheon 22212, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Astray
Water 2021, 13(4), 437; https://doi.org/10.3390/w13040437
Received: 4 January 2021 / Revised: 25 January 2021 / Accepted: 1 February 2021 / Published: 8 February 2021
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology)
Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results. View Full-Text
Keywords: deep learning; hourly runoff prediction; sequence-to-sequence structure deep learning; hourly runoff prediction; sequence-to-sequence structure
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MDPI and ACS Style

Han, H.; Choi, C.; Jung, J.; Kim, H.S. Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation. Water 2021, 13, 437. https://doi.org/10.3390/w13040437

AMA Style

Han H, Choi C, Jung J, Kim HS. Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation. Water. 2021; 13(4):437. https://doi.org/10.3390/w13040437

Chicago/Turabian Style

Han, Heechan; Choi, Changhyun; Jung, Jaewon; Kim, Hung S. 2021. "Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation" Water 13, no. 4: 437. https://doi.org/10.3390/w13040437

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