灰色关联—集对聚类预测模型在吉林省用水量预测中的应用  

Application of grey correlation degree-set pair analysis classified prediction method on water consumption prediction of Jilin Province

作　　者：宋帆 杨晓华[1] 武翡翡 孙波扬 耿雷华[2] SONG Fan;YANG Xiaohua;WU Feifei;SUN Boyang;GENG Leihua(State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing 100875,China;Nanjing Hydraulic Research Institute,Nanjing 210029,China)

机构地区：[1]北京师范大学环境学院水环境模拟国家重点实验室,北京100875 [2]南京水利科学研究院,江苏南京210029

出　　处：《水资源与水工程学报》2018年第3期28-33,共6页Journal of Water Resources and Water Engineering

基　　金：国家重点研发计划项目(2017YFC0506603;2016YFC0401305);国家自然科学基金项目(41530635;51379013;51679007)

摘　　要：对地区未来用水量进行预测对于实现水资源的合理规划与调度有着重要意义。为了对吉林省未用水量进行合理预测,建立了吉林省短期用水量预测的灰关联-集对聚类预测模型,并用吉林省实际用水量数据对模型进行了交叉精度检验。结果发现:该模型对吉林省2015用水量预测结果与实际数据的相对误差为2.00%,预测精度好于灰色预测模型和BP神经网络模型。20年数据检验平均误差为2.675%,预测效果较好,可用于区域未来用水量预测。根据此模型以及吉林省发展规划,2020年吉林省用水量将达到138.74×10~8m^3。Reasonable prediction of future water demand is of great significance to realize the rational planning and scheduling of water resources. In order to predict the short-term water consumption accurately in Jilin province,the grey correlation degree-set pair analysis classified prediction model（ GCD-SPACPM） was set up. And the accuracy of the model prediction was cross-checked by the actual water consumption of Jilin Province. Results showed that the relative error of the prediction data to actual data in 2015 was 2. 00%,indicating a better prediction than Grey Prediction model and BP neural network.The mean error of data inspection of two decades was 2. 675%,the predicting effect was good and it can be used for the regional water consumption prediction. According to this model and the development plan,the amount of water consumption of Jilin Province will reach 13. 84 billion cubic meters by 2020.

关 键 词：用水量预测 灰色关联度 集对分析 聚类预测 联系度 吉林省 

分 类 号：TV212.2[水利工程—水文学及水资源] TU991.31[建筑科学—市政工程]