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NTIS 바로가기上下水道學會誌 = Journal of Korean Society of Water and Wastewater, v.34 no.1, 2020년, pp.35 - 43
박정수 (국립한밭대학교 건설환경공학과) , 이현호 (한국수자원공사 데이터센터)
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...
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핵심어 | 질문 | 논문에서 추출한 답변 |
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순환신경망의 단점을 보완하는 방법은? | , 2016). LSTM은 이러한 RNN의 단점을 보완하기 위해 이전 단계의 정보를 기억하는 메모리를 가지도록 구성되어 있으며 이는 오랫동안 기억하고 전달할 필요가 있는 정보를 저장하는 셀(cell) state와, 현 시점에서 단기간에 활용되는 정보를 저장하는 은닉(hidden) state로 구분된다 (Greffet al., 2016; Hochreiter and Schmidhuber, 1997). | |
LSTM란? | LSTM(Long-Short Term Memory)은 이전 단계의 정보를 기억하는 장단기 메모리를 가지도록 구성되어있어 시계열 분석 등에 좋은 성능을 보이는 최신 딥러닝 알고리즘중 하나로, 최근 LSTM을 수질 예측에 활용하기 위한 연구에 대한 관심이 높아지고 있다. Lee and Lee (2018)는 한국의 16개보의 일일 및 주간 수질 측정 자료의 pH, 생물화학적산소요구량, 화학적 산소요구량, 용존산소, 수온을 이용하여, Chl-a 발생을 예측하는데 MLP(multilayer perceptron)와 순환신경망 모형(RNN: recurrent neural network) 그리고 LSTM을 적용하고 이중 LSTM이 가장 좋은 예측 성능을 가짐을 보여주었으며, Zhou et al. | |
순환신경망의 단점은? | 딥러닝 알고리즘중 하나인 순환신경망(RNN: Recurrent Neural Network)은 시계열 자료와 같이 연속된 순서를 가진 자료의 분석에 좋은 성능을 보이는 것으로 알려져 있으나, 은닉층(hidden layer)이 늘어남에 따라 오차 보정을 위한 역전파(backpropagation)시 기울기 손실(vanishing gradient)이 발생하여 학습능력이 감소하는 단점을 가지고 있다 (Greff et al., 2016). |
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