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http://dx.doi.org/10.30693/SMJ.2022.11.2.31

Nakdong River Estuary Salinity Prediction Using Machine Learning Methods  

Lee, Hojun (동아대학교 컴퓨터AI공학부 컴퓨터공학과)
Jo, Mingyu (동아대학교 컴퓨터AI공학부 컴퓨터공학과)
Chun, Sejin (동아대학교 컴퓨터AI공학부)
Han, Jungkyu (동아대학교 컴퓨터AI공학부)
Publication Information
Smart Media Journal / v.11, no.2, 2022 , pp. 31-38 More about this Journal
Abstract
Promptly predicting changes in the salinity in rivers is an important task to predict the damage to agriculture and ecosystems caused by salinity infiltration and to establish disaster prevention measures. Because machine learning(ML) methods show much less computation cost than physics-based hydraulic models, they can predict the river salinity in a relatively short time. Due to shorter training time, ML methods have been studied as a complementary technique to physics-based hydraulic model. Many studies on salinity prediction based on machine learning have been studied actively around the world, but there are few studies in South Korea. With a massive number of datasets available publicly, we evaluated the performance of various kinds of machine learning techniques that predict the salinity of the Nakdong River Estuary Basin. As a result, LightGBM algorithm shows average 0.37 in RMSE as prediction performance and 2-20 times faster learning speed than other algorithms. This indicates that machine learning techniques can be applied to predict the salinity of rivers in Korea.
Keywords
Machine Learning; Salinity Prediction; Nakdong River; Decision tree;
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Times Cited By KSCI : 4  (Citation Analysis)
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