Research on water quality prediction model based on echo state network

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.