摘要
以南京市为例,利用1999-2010年的总用水量数据,采用主成分分析法对影响水资源需求量的9个因子进行主要影响因子分析,根据确定的主要影响因子构造BP神经网络的输入样本,从而进行不同水平的年总需水量预测。结果表明:人口、GDP、万元GDP用水量、人均水资源量、污水年排放量为影响研究区需水量的主要因子,将此作为主要因子构造BP神经网络的输入样本,确定网络输入节点数,建立南京市总需水量预测模型。模拟计算结果表明,基于主成分分析的BP神经网络模型,预测结果的平均误差小于0.2亿m3。
Taking the water demand data from 1999 to 2010 of Nanjing for example,this paper analyzes the main factors that influence the water resource quantity based on the principle component analysis method.According to these main factors,the input samples of BP neutral network are determined.Thereby,the BP neutral networks can be trained to predict.The results show that population,GDP,water consumption of ten thousand yuan GDP,water resources per capita and volume of sewage discharge per year are the primary indexes that affect water resource demand.The corresponding prediction modeling outcome shows that the simulated experiment is quite fit for the practical situation and the average error of prediction is less than 0.2×108 m3.
出处
《水资源与水工程学报》
2012年第6期6-9,共4页
Journal of Water Resources and Water Engineering
基金
国家自然科学基金项目(41171430
40771044)
关键词
需水预测
主成分分析法
BP神经网络
water demand prediction
principle component analysis
BP neutral networks
