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Article

Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter

1
Department of Water Resources Engineering, Kasetsart University, Bangkok 10900, Thailand
2
Department of Industrial Engineering, Kasetsart University, Bangkok 10900, Thailand
*
Author to whom correspondence should be addressed.
Academic Editor: Fangxin Fang
Water 2022, 14(18), 2898; https://doi.org/10.3390/w14182898
Received: 11 August 2022 / Revised: 8 September 2022 / Accepted: 13 September 2022 / Published: 16 September 2022
(This article belongs to the Section Hydrology)
Flood forecasting is among the most important precaution measures to prevent devastating disasters affecting human life, properties, and the overall environment. It is closely involved with precipitation and streamflow data forecasting tasks. In this work, we introduced a multi-step discharge prediction framework based on deep learning models. A simple feature representation technique using a correlation of backward lags was enhanced with a time of concentration (TC) concept. Recurrent neural networks and their variants, coupled with the TC-related features, provided superior performance with over 0.9 Nash–Sutcliffe model efficiency coefficient and substantially high correlation values for multiple forecasted points. These results were consistent among both the Upper Nan and the Loei river basins in Thailand, which were used as case studies in this work. View Full-Text
Keywords: discharge prediction; flood forecasting; time of concentration; deep learning; recurrent neural networks discharge prediction; flood forecasting; time of concentration; deep learning; recurrent neural networks
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MDPI and ACS Style

Thaisiam, W.; Saelo, W.; Wongchaisuwat, P. Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter. Water 2022, 14, 2898. https://doi.org/10.3390/w14182898

AMA Style

Thaisiam W, Saelo W, Wongchaisuwat P. Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter. Water. 2022; 14(18):2898. https://doi.org/10.3390/w14182898

Chicago/Turabian Style

Thaisiam, Wandee, Warintra Saelo, and Papis Wongchaisuwat. 2022. "Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter" Water 14, no. 18: 2898. https://doi.org/10.3390/w14182898

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