基于主成分回归与灰色神经网络模型的水资源承载力需水量预测

我国的水资源利用问题日趋严峻。作为影响水资源承载力的重要因素,社会经济活动对水资源的影响尤为明显。通过分析影响水资源承载力的社会经济驱动要素,建立主成分回归模型,分析得出影响江苏省水资源承载力变化的三个驱动力以及驱动力影响度,利用三个驱动力中的6个重要驱动因子,建立灰色神经网络预测模型,预测出江苏省2012-2013年的年需水量。结果表明:预测模型精度较高,最后结合江苏省发展现状提出相关的政策性建议。

Forecast of water requirement of water resources carrying capacity based on regression of principal components and grey neural network model

Our country is facing serious water problems. As the important factor that affect water resources carrying capacity, the influence of social and economic activities on water resources is obvious. By analyzing the key element of socio economic driver that affects water resources carrying capacity, establishing the principal component regression model , the driving factors which influence the carrying capacity of water resources in Jiangsu were classified into three kinds , the influence degree of the driving factors was gained. Combined with grey neural network theory and six important factors which is coming from the three main factors, the grey neural network model was established to predict the total water uses in Jiangsu Province from 2012 to 2013.The prediction accuracy of the model is higher.At last, the paper gave relative policy recommendations by combining with the actual situation of Jiangsu Province.