基于动态隶属度的模糊时间序列模型的水质预测研究  

Water quality prediction based on fuzzy time series model of dynamic membership degree

作　　者：赵春兰[1,2] 李屹 何婷 武刚 王兵[4] ZHAO Chun-lan;LI Yi;HE Ting;WU Gang;WANG Bing(School of Sciences,Southwest Petroleum University,Chengdu 610500;Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu 610500;Dagang Oil Field Branch,PetroChina Company Limited,Tianjin 300280;School of Computer Science,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学理学院,四川成都610500 [2]西南石油大学人工智能研究院,四川成都610500 [3]中国石油天然气股份有限公司大港油田分公司,天津300280 [4]西南石油大学计算机科学学院,四川成都610500

出　　处：《计算机工程与科学》2022年第8期1488-1496,共9页Computer Engineering & Science

基　　金：国家科技重大专项(2016ZX05021-006)。

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。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.

关 键 词：水质预测 季节效应 模糊时间序列 动态隶属度 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]