基于主成分分析的BP神经网络对南京市水资源需求量预测

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.

Forecast of water demand by using BP neutral network based on principle component analysis in Nanjing

Taking the water demand data from 1999to 2010of 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 0.2×10^8m3.