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Open AccessArticle

Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models

1
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea
2
Risk Management Office, KB Claims Survey and Adjusting, Seoul 06212, Korea
3
Department of Civil Engineering, Kyung Hee University, Yongin 17104, Korea
4
Graduate School of Disaster Prevention, Kangwon National University, Samcheok 25913, Korea
5
Department of Civil and Environmental Engineering, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3022; https://doi.org/10.3390/w12113022
Received: 12 September 2020 / Revised: 22 October 2020 / Accepted: 26 October 2020 / Published: 28 October 2020
(This article belongs to the Section Hydrology and Hydrogeology)
With recent increases of heavy rainfall during the summer season, South Korea is hit by substantial flood damage every year. To reduce such flood damage and cope with flood disasters, it is necessary to reliably estimate design floods. Despite the ongoing efforts to develop practical design practice, it has been difficult to develop a standardized guideline due to the lack of hydrologic data, especially flood data. In fact, flood frequency analysis (FFA) is impractical for ungauged watersheds, and design rainfall–runoff analysis (DRRA) overestimates design floods. This study estimated the appropriate design floods at ungauged watersheds by combining the DRRA and watershed characteristics using machine learning methods, including decision tree, random forest, support vector machine, deep neural network, the Elman recurrent neural network, and the Jordan recurrent neural network. The proposed models were validated using K-fold cross-validation to reduce overfitting and were evaluated based on various error measures. Even though the DRRA overestimated the design floods by 160%, on average, for our study areas the proposed model using random forest reduced the errors and estimated design floods at 99% of the FFA, on average. View Full-Text
Keywords: design flood; machine learning; rainfall; ungauged watershed; random forest design flood; machine learning; rainfall; ungauged watershed; random forest
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MDPI and ACS Style

Lee, J.-Y.; Choi, C.; Kang, D.; Kim, B.S.; Kim, T.-W. Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models. Water 2020, 12, 3022. https://doi.org/10.3390/w12113022

AMA Style

Lee J-Y, Choi C, Kang D, Kim BS, Kim T-W. Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models. Water. 2020; 12(11):3022. https://doi.org/10.3390/w12113022

Chicago/Turabian Style

Lee, Jin-Young; Choi, Changhyun; Kang, Doosun; Kim, Byung S.; Kim, Tae-Woong. 2020. "Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models" Water 12, no. 11: 3022. https://doi.org/10.3390/w12113022

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