Soyang river dam monthly inflow prediction using extreme learning machine†
ByungSik Kim1 · SeungCheol Choi22 · ByungHyun Lee3 · HernJoong Ha4
1Department of Artificial Intelligence & Software/Graduate School of Disaster Prevention, Kangwon National University
23AI for Climate & Disaster Management Center, Kangwon National University
4NGS Co., Ltd.
23AI for Climate & Disaster Management Center, Kangwon National University
4NGS Co., Ltd.
Correspondence to: † This research was supported by a grant(2021-MOIS37-001) from Intelligent Technology Development Program on Disaster Response and Emergency Management funded by Ministry of Interior and Safety of Korean government (MOIS, Korea).
1 Professor, Department of Artificial Intelligence & Software/Graduate School of Disaster Prevention, Kangwon National University, Samcheok 25913, Korea.
2 Researcher, AI for Climate & Disaster Management Center, Kangwon National University, Samcheok 25913, Korea.
3 Research professor, AI for Climate & Disaster Management Center, Kangwon National University, Samcheok 25913, Korea.
4 Director, NGS Co., Ltd., Anyang 14058, Korea. E-mail: insugolf@naver.com
1 Professor, Department of Artificial Intelligence & Software/Graduate School of Disaster Prevention, Kangwon National University, Samcheok 25913, Korea.
2 Researcher, AI for Climate & Disaster Management Center, Kangwon National University, Samcheok 25913, Korea.
3 Research professor, AI for Climate & Disaster Management Center, Kangwon National University, Samcheok 25913, Korea.
4 Director, NGS Co., Ltd., Anyang 14058, Korea. E-mail: insugolf@naver.com
Received January 18, 2024; Revised March 23, 2024; Accepted March 26, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- In recent years, the frequency of flooding due to heavy rainfall has been increasing due to climate change, highlighting the growing importance of disaster prevention. Accurate prediction of dam inflow is crucial during heavy rainfall and typhoons for proper dam discharge. To simulate inflow, various approaches, including physical models and machine learning models, are employed. In this study, we utilized the Extreme Learning Machine (ELM), a machine learning technique, to simulate the inflow of Soyang River Dam watershed using precipitation data observed at the weather station in Inje (Station 211) and historical inflow data. Data were collected from January 1974 to August 2023, with training using data from January 1974 to December 2020 and validation with data from January 2021 to August 2023. Additionally, we compared the results of ELM with those of a Multilayer Perceptron (MLP) with a similar structure and conducted model validation using evaluation metrics. The proposed ELM model showed validation results for the test data, achieving an MAE of 19.98, MSE of 931.25 and R-squared value of 0.83.
- Keywords : Dam monthly inflow, extreme learning machine, machine learning, monthly average temperature, monthly precipitation
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