[논문]순환신경망 모델을 활용한 팔당호의 단기 수질 예측

한지우; 조용철; 이소영; 김상훈; 강태구

doi:10.15681/kswe.2023.39.1.46

순환신경망 모델을 활용한 팔당호의 단기 수질 예측
Short-Term Water Quality Prediction of the Paldang Reservoir Using Recurrent Neural Network Models 원문보기

한국물환경학회지 = Journal of Korean Society on Water Environment, v.39 no.1, 2023년, pp.46 - 60

한지우 (국립환경과학원 한강물환경연구소) , 조용철 (국립환경과학원 한강물환경연구소) , 이소영 (국립환경과학원 한강물환경연구소) , 김상훈 (국립환경과학원 한강물환경연구소) , 강태구 (국립환경과학원 한강물환경연구소)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (18)

그림 Fig. 1. (a) Monitoring Sites in the Paldang Reservoir (PD2) and (b) the sensor at PD2.
표 Table 1. Training and test set for each period
표 Table 2. Features of each gate in LSTM
표 Table 3. Features of each gate in GRU
표 Table 4. Input variables used in the models
표 Table 5. Distribution characteristics of water quality parameters by period
그림 Fig. 2.Predicted performance results of the models for test data before feature importance (Period 1).
표 Table 6. Predicted performance results of the models for test data before feature importance (Period 1)
그림 Fig. 3. Predicted performance results of the models for test data before feature importance (Period 2).
표 Table 7. Predicted performance results of models for test data before feature importance(Period 2).
그림 Fig. 4. Rank of important variables for each water quality parameter.
표 Table 8. The rank of important variables of each water quality parameters
그림 Fig. 5. Predicted performance results of the models for test data after feature importance (Period 1).
표 Table 9. Predicted performance results of the models for test data after feature importance (Period 1)
그림 Fig. 6. Predicted performance results of the models for test data after feature importance (Period 2).
표 Table 10. Predicted performance results of the models for test data after feature importance (Period 2)
그림 Fig. 7. Comparison of model predictive power before and after feature importance in Period 1.
그림 Fig. 8. Comparison of model predictive power before and after feature importance in period 2.

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