Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
By Di Zhang, Junqiang Lin, Qidong Peng, Dongsheng Wang, Tiantian Yang, Soroosh Sorooshian, Xuefei Liu, Jiangbo Zhuang. Journal of Hydrology, 2018
303 citations recorded by the source corpus.
Abstract Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period.
Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/3253cb434e792d5e28fe08b58317d74bd6ea5525
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