Abstract: Scientific and effective water quality prediction is especially important for water resources management and water pollution early warning. The accuracy of the traditional prediction model is somewhat limited by the existence of nonlinearity, non-smoothness, fuzziness, seasonality and other characteristics of water quality index series. This paper combines the characteristics of autoregressive integrated moving average ARIMA model and classical fuzzy time series model, and proposes a new model of fuzzy time series water quality prediction based on dynamic affiliation. First, the use of fuzzy C-mean clustering from the original data to build the affiliation series; second, the use of the classical time series model to predict different sub-subordination series to get the dynamic affiliation; finally, defuzzification to get the predicted value of water quality indicators. The new proposed model in this paper was applied to short-term prediction of water quality indexes at a section of Minjiang River, and compared with the classical fuzzy time series model, ARIMA multiplicative seasonal model. The experimental results show that the RMSE, MAPE and MAE of the new model are better than the classical fuzzy time series model and ARIMA multiplicative seasonal model, so the new prediction model greatly improves the prediction accuracy and can provide valuable reference for water pollution prevention and control.

Key words: water quality prediction, seasonal effect, fuzzy time series, dynamic membership