{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:47:23Z","timestamp":1776811643407,"version":"3.51.2"},"reference-count":19,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/jcm-225954","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T13:24:57Z","timestamp":1646141097000},"page":"901-910","source":"Crossref","is-referenced-by-count":2,"title":["Research on water quality prediction model based on echo state 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