【摘要】 科学有效的水质预测对于水资源的管理与水污染预警尤为重要。由于水质指标序列存在非线性、非平稳性、模糊性和季节性等特点，传统预测模型的精度受到一定的限制。结合差分整合自回归移动平均ARIMA模型和经典模糊时间序列模型的特性，提出了一种基于动态隶属度的模糊时间序列水质预测新模型。首先，利用模糊C均值聚类从原始数据中构建隶属度序列；其次，利用经典的时间序列模型对不同的子隶属度序列进行预测，得到动态隶属度；最后，去模糊化得到水质指标的预测值。应用提出的新模型对岷江某断面的水质指标进行了短期预测，并与经典模糊时间序列模型和ARIMA乘积季节模型进行对比。实验结果表明，新模型在RMSE、MAPE和MAE上均优于经典模糊时间序列模型和ARIMA乘积季节模型，极大地提高了预测精度，可为水污染防治提供有价值的参考。更多还原

【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.更多还原