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http://dx.doi.org/10.7837/kosomes.2022.28.2.193

Preliminary Study on the Reproduction of Dissolved Oxygen Concentration in Jinhae Bay Based on Deep Learning Model  

Park, Seongsik (Department of Ocean Engineering Pukyong National University)
Kim, Kyunghoi (Department of Ocean Engineering Pukyong National University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.2, 2022 , pp. 193-200 More about this Journal
Abstract
We conducted a case study to determine the optimal model parameters and predictors of Long Short-Term Memory (LSTM) for the reproduction of dissolved oxygen (DO) concentration in Jinhae Bay. The model parameter case study indicated the lowest accuracy when the Hidden node=10, Epoch=100. This was caused by underfitting of machine learning. The accuracy increased as the Hidden node and Epoch increased. The accuracy was the highest when the Hidden node=80 and Epoch=100 with R2=0.99. In the bottom DO reproduction of Step 1 of the predictors case study, accuracy was highest when the water temperature was used as a predictor with R2=0.81. In Step 2, The R2 value increased up to 0.92 when the water temperature and SiO2 were used as a predictor. This was caused by a high correlation between the bottom DO and SiO2 concentrations. Consequently, we determined the optimal model parameters and predictors of LSTM for the reproduction of DO concentration in Jinhae Bay.
Keywords
Long short-term memory; Machine learning; Deep learning; Jinhae Bay; Dissolved oxygen; Reproduction;
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