Affiliations: School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, China
Correspondence:
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Corresponding author: Yan Kang, School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei 066000, China. E-mail: [email protected].
Abstract: Using artificial neural network (ANN) to solve the problem of time series water quality prediction has become increasingly mature. In this paper, through the study of leaky-integral echo state neural network (Leaky ESN), combined with the historical water quality data of Dongzhen Reservoir in Fujian Province, a single-day water quality prediction model was constructed, and the Bayesian optimization algorithm was used to realize the automatic optimization of hyper-parameters in the network. On this basis, multi-day prediction models were constructed by further improving the network, which used the historical water quality data of the previous 7 days to predict the water quality of the next 3 days, 5 days and 7 days. Then the prediction models were applied to the water quality prediction of the study. The experimental results show that the single-day prediction model with Bayesian optimization has high accuracy. The multi-day prediction models can also achieve good prediction effect, and have more practical application value. They are more suitable for early warning of water quality.
