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http://dx.doi.org/10.7837/kosomes.2020.26.4.382

Prediction of Dissolved Oxygen in Jindong Bay Using Time Series Analysis  

Han, Myeong-Soo (GeoSystem Research Corporation)
Park, Sung-Eun (National Institute of Fisheries Science)
Choi, Youngjin (GeoSystem Research Corporation)
Kim, Youngmin (National Institute of Fisheries Science)
Hwang, Jae-Dong (National Institute of Fisheries Science)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.26, no.4, 2020 , pp. 382-391 More about this Journal
Abstract
In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.
Keywords
Oxygen depleted water; Prediction; A.I. (Artificial Intelligence); ARIMA; LSTM;
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