논문 상세보기
pp.35-43
Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.
1. 서 론
2. 재료 및 실험방법
2.1 LSTM 모형 개요
2.2 LSTM 모형 구축
2.3 입력 데이터
2.4 LSTM 모형 성능 검정 및 비교
3. 결과 및 고찰
3.1 모형 성능 평가
3.2 고탁수 발생 예측 성능 평가
3.3 Time step에 따른 모형 성능 비교
3.4 유량-탁도 관계의 이격현상 분석 및 모형 개선 방안
3.5 딥러닝 모형의 최적화를 위한 데이터 확보
4. 결 론
References
