水利水电技术2017，Vol.48Issue(7)：1-6,6.DOI:10.13928/j.cnki.wrahe.2017.07.001
基于PCA-RBF神经网络模型的城市用水量预测
PCA-RBF neural network model-based urban water consumption prediction
摘要
Abstract
Aiming at the problems,such as many factors of influence on the urban water consumption,strong correlation and slow convergence speed of BP neural network,being prone to trapping in the local minima,etc.,the urban water consumption is predicted herein by means of combining the principal components analysis (PCA) with the RBF neural network;for which the dimension reduction is made on the factors of the influence on the water consumption for eliminating the multicollinearity,and then the former three principal components which can replace the original factors of the influence on the water consumption are selected as the input factors,while the RBF neural network with quick learning and convergence speed and strong capacity of mode identification is selected for the prediction.The study result shows that the mean relative errors of the model are minimum within the phases of the training and the prediction,which are 0.165 4％ and 0.677 5％ respectively,while both the earning and prediction capacities are better than those from the models of RBP and BP neural networks,thus enhance both the convergence speed and the prediction accuracy;from which the number of the principal components increases from 3 to 5 along with the increase of the contribution rate of information accumulation from 93.09％ to 98.37％ and the decreases of the mean relative error from 0.250 7％to 0.206 0％,thus the prediction accuracy is slightly enhanced.During the prediction made on the water consumption of Zaozhuang City from 2015 to 2020,the total water consumption therein is increased with small amplitude at first,and then is decreased,which shows an increase with the shape of inversed U.Generally,this model has reference value for the urban regional water resources planning concerned.关键词
城市用水量预测/主成分分析/RBF神经网络/BP神经网络/主成分数量/需水预测Key words
urban water consumption prediction/principal component analysis/RBF neural network/BP neural network/number of principal components/prediction of water demand分类
建筑与水利引用本文复制引用
高学平,陈玲玲,刘殷竹,孙博闻..基于PCA-RBF神经网络模型的城市用水量预测[J].水利水电技术,2017,48(7):1-6,6.基金项目
“十二五”国家科技支撑计划项目(2015BAB07B02) （2015BAB07B02）
国家自然科学基金创新群体基金项目(51621092) （51621092）
国家自然科学基金(51609166) （51609166）
