人民珠江2024，Vol.45Issue(7)：92-100,9.DOI:10.3969/j.issn.1001-9235.2024.07.011
基于数据分解与NARX优化的滇池CODMn时间序列预测
Time Series Prediction of CODMn in Dianchi Lake Based on Data Decomposition and NARX Optimization
摘要
Abstract
The permanganate index(CODMn)is one of the important indicators for measuring the degree of pollution of water bodies by reducing substances.To improve the prediction accuracy of CODMn,a WPT-SHIO-NARX CODMn time series prediction model is proposed,which combines wavelet packet transform(WPT),success history intelligent optimization(SHIO)algorithm,and nonlinear autoregressive neural network(NARX).Firstly,WPT is used to decompose the CODMn time series into one periodic component and three fluctuation components;Then,the principle of SHIO is briefly introduced,and it is used to optimize hyperparameters such as NARX input delay order;Finally,based on the hyperparameters obtained through optimization,the WPT-SHIO-NARX model is established to predict the periodic and fluctuation components of CODMn.After reconstruction,the final prediction results are obtained.Comparative analyses are made with WPT-particle swarm optimization(PSO)-NARX,WPT-genetic algorithm(GA)-NARX,WPT-NARX,SHIO-NARX,WPT-SHIO extreme learning machine(ELM),and WPT-SHIO-BP neural network models.The models are validated using weekly CODMn monitoring data from 2004 to 2015 at the Xiyuan Tunnel and Guanyin Mountain sections of Dianchi Lake.The results show that the WPT-SHIO-NARX model has good predictive performance,with mean absolute percentage error(MAPE)of 0.108%and 0.045%,0.151%and 0.165%for the next 1 week and 2 weeks(half a month)of CODMn prediction at Xiyuan Tunnel and Guanyin Mountain,respectively.The MAPE for the next 4 weeks(January)of CODMn prediction is 1.383%and 0.809%,and the MAPE for the next 8 weeks(February)of CODMn prediction is 6.180%and 4.573%,respectively.The prediction accuracy is higher than other comparative models;WPT can decompose CODMn time series data into more regular subsequence components,improving the model's prediction accuracy;SHIO can effectively optimize NARX hyperparameters,significantly improving NARX performance,with optimization effects superior to GA and PSO;the NARX network has delay and feedback mechanisms,making it more suitable for time series prediction,and its predictive performance is better than that of ELM and BP networks.关键词
CODMn预测/非线性自回归神经网络/成功历史智能优化算法/小波包变换/滇池Key words
CODMn forecast/nonlinear autoregressive neural network/success history intelligent optimization algorithm/wavelet packet transform/Dianchi Lake分类
建筑与水利引用本文复制引用
王永顺,崔东文..基于数据分解与NARX优化的滇池CODMn时间序列预测[J].人民珠江,2024,45(7):92-100,9.基金项目
云南省创新团队建设专项(2018HC024) （2018HC024）
云南重点研发计划(科技入滇专项) （科技入滇专项）
国家澜湄合作基金项目(2018-1177-02) （2018-1177-02）
