摘要
为了建立适应于洞庭湖区域地下水资源量变化规律的预测模型,在分析洞庭湖区域河川天然径流量、长江三口入水量、城陵矶出水量与地下水资源量相关性的基础上,分别利用多因子逐步回归模型、BP神经网络模型和多变量时间序列CAR模型建立了3种洞庭湖区域地下水资源量预测模型,并对所建立的3种模型的预测精度和预测结果整体规律进行了对比分析。研究结果表明:地下水资源量与河川天然径流量、长江三口入水量、城陵矶出水量具有较好的相关性;多变量时间序列CAR模型的预测精度较好,BP神经网络模型的预测精度次之,而多因子逐步回归模型的预测精度较差;多变量时间序列CAR模型的预测结果整体规律优于BP神经网络模型,而BP神经网络模型的预测结果整体规律则优于多因子逐步回归模型。
In order to establish the predictive model adapt to the variation law of groundwater resources in the region of the Dongting Lake , on the basis of analyzing the relativity of nature river flow , Yangtze three inlet inflow water and Chenglingji outflow water with water resources , the paper respectively used multi-factor stepwise regression model , back-propagation neural network model and multivariate time series CAR model to set up three kinds of predictive model of groundwater resources in Dongting Lake area and compared and analyzed the overall law of prediction accuracy and prediction results of the three models . The results showed that the amount of groundwater resources have better relativity with the natural river flow, Yangtze three inlet inflow water resources , and Chenglingji outflow water; the prediction accuracy of CAR model of multivariate time series is better ,and then that of Back-Propagation Neural Network is the second , and the prediction accuracy of multi-factor stepwise regression model is poor;the overall law of the prediction results of multivariate time series CAR model is better than that of back-propagation neu-ral network model , and the overall law of prediction result of Back-Propagation neural network model is superior to that of multi-factor stepwise regression model .
出处
《水资源与水工程学报》
2015年第5期46-50 55,55,共6页
Journal of Water Resources and Water Engineering
基金
国家自然科学基金项目(51278067)
湖南省科学技术厅科技计划重点项目(2013FJ2008)
关键词
多因子逐步回归
BP神经网络
多变量时间序列
地下水资源量预测
洞庭湖区域
multi-factor stepwise regression
back-propagation neural network
multivariate time series
prediction of groundwater resources
Dongting lake area
