基于改进PCA-BP神经网络模型的海宁市需水预测  

Water Demand Prediction in Haining City Based on Improved PCA-BP Neural Network Mod

作　　者：杨登元 鞠茂森[2] 唐德善[1] YANG Deng-yuan;JU Mao-sen;TANG De-shan(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;Research and Training Center for River Chief System,Hohai University,Nanjing 210098,China)

机构地区：[1]河海大学水利水电学院,江苏南京210098 [2]河海大学河长制研究与培训中心,江苏南京210098

出　　处：《水电能源科学》2024年第5期68-71,79,共5页Water Resources and Power

基　　金：国家重点研发计划(2017YFC0405805-04)。

摘　　要：需水预测是地区水资源规划中的重要部分,对于实现水资源合理有序开发,保障社会经济的可持续发展有重要的指导意义。采用改进PCA-BP神经网络模型对影响需水量的9个影响因子进行降维处理,并分别以海宁市2001~2014、2015~2020年数据作为训练样本和检验样本完成模型训练,其中,综合灰色预测模型GM(1,1)对降维后的影响因子独立预测,从而预测海宁市规划年需水量,并与传统定额法的需水预测结果进行对比分析。结果表明,人口、GDP、居民生活用水量、城镇公共用水量为影响海宁市需水量的主要因子;通过构建改进PCA-BP神经网络模型得到的2025、2030、2035年需水结果,比传统定额法更为真实、合理,进一步证实了预测模型的合理性,可为海宁市未来水资源规划提供指导。Water demand prediction is an important part of regional water resources planning,which is of great sig-nificance for realizing the rational and orderly development of water resources and guaranteeing the sustainable develop-ment of society and economy.The article adopts the improved PCA-BP neural network model to reduce the dimensionali-ty of the nine influencing factors affecting water demand,and completes the training of the model with the data of Haining City in 2001-2014 and 2015-2020 as the training samples and test samples,respectively,in which the integrated grey pre-diction model GM(1,1)independently predicts the influencing factors after the reduction of dimensionality so as to predict the annual water demand of Haining City in the planning year.Finally,the traditional quota method is used to predict the annual water demand of the planning year and its comparative analysis.The results show that the population,GDP,resi-dential water consumption,urban public water consumption are the main factors affecting the water demand of Haining City;The water demands in 2025,2030 and 2035 obtained by the improved PCA-BP neural network model are more real-istic and reasonable than the traditional quota method,which further confirms the reasonableness of the prediction model and can provide the corresponding guidance for the future water resources planning of Haining City.

关 键 词：需水预测 主成分分析法 改进PCA-BP神经网络 灰色预测模型 

分 类 号：TV213.4[水利工程—水文学及水资源]