Abstract: The research aims to tackle the difficulty in obtaining the mechanism of water quality change in complex waters and modeling water quality prediction as well as the low accuracy of prediction. A water quality prediction method integrating Autoregressive Integrated Moving Average (ARIMA) model and Support Vector Machine Regression (SVR) model is established. After pretreatment, the data are linearly fitted by ARIMA model, and then the residual is predicted by SVR model to compensate for the non-linear change. The monitoring data of pH value and dissolved oxygen in Chaohu Lake basin from 2004 to 2015 are selected as experimental samples. Analysis by HP filtering method shows that the two sets of data have different trends and fluctuation characteristics. The prediction effect of the hybrid model is analyzed through comparative study on evaluation indicators. The results conclude that the present model has high accuracy with the correlation coefficients between predicted values and observed values of pH value and dissolved oxygen both reaching 0.99, the root mean square error 0.20 and 0.61, and the mean absolute percentage error 2.2% and 6.6%, respectively. The ARIMA-SVR hybrid model is of high prediction accuracy and strong generalization ability, suitable for water quality prediction in complex waters.

Key words: water quality prediction, ARIMA, SVR, hybrid model, dissolved oxygen