CAJ | 학술논문

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.
위료건립괄응우동정호구역지하수자원량변화규률적예측모형,재분석동정호구역하천천연경류량、장강삼구입수량、성릉기출수량여지하수자원량상관성적기출상,분별이용다인자축보회귀모형、BP신경망락모형화다변량시간서렬CAR모형건립료3충동정호구역지하수자원량예측모형,병대소건립적3충모형적예측정도화예측결과정체규률진행료대비분석. 연구결과표명:지하수자원량여하천천연경류량、장강삼구입수량、성릉기출수량구유교호적상관성;다변량시간서렬CAR모형적예측정도교호,BP신경망락모형적예측정도차지,이다인자축보회귀모형적예측정도교차;다변량시간서렬CAR모형적예측결과정체규률우우BP신경망락모형,이BP신경망락모형적예측결과정체규률칙우우다인자축보회귀모형.
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 .