| Title |
Short-Term Water Quality Prediction of the Paldang Reservoir Using Recurrent Neural Network Models |
| Authors |
한지우 ( Jiwoo Han ) ; 조용철 ( Yong-chul Cho ) ; 이소영 ( Soyoung Lee ) ; 김상훈 ( Sanghun Kim ) ; 강태구 ( Taegu Kang ) |
| DOI |
https://doi.org/10.15681/KSWE.2023.39.1.46 |
| Keywords |
Feature Importance; GRU; LSTM; Random Forest; Recurrent Neural Network; Water Quality |
| Abstract |
Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period. |