水科学进展2017，Vol.28Issue(3)：415-420,6.DOI:10.14042/j.cnki.32.1309.2017.03.012
基于主成分-时间序列模型的地下水位预测
Groundwater level forecast based on principal componentanalysis and multivariate time series model
摘要
Abstract
Predication of groundwater level is an important basis for the management of regional water resources.Based on the high randomness and hysteresis characteristics of groundwater in time series, a groundwater level prediction model that is based on principal component analysis and multivariable time series CAR model is built and used for the predication of groundwater level at Dougou irrigation area of Ji'nan.According to the results, the determination coefficient R2 and the Nash-Suttcliffe coefficient Ens of the simulated value and the measured value all reached 0.90 and the above.By taking 2011 as the base year, when precipitation reduces 10%-20%, evaporation and domestic water consumption increases 10%-20% and 273 900-1 370 000 m3 surface water is diverted for agricultural irrigation, the groundwater level at the irrigation area will be maintained at 30.99-31.29 m in 2030, increasing 0.12-0.42 m than that of the base year.Under the background of regional water resources shortage, proper diverting surface water for irrigation and reducing groundwater exploitation can gradually increase the groundwater level at irrigation area and have great significance for the sustainable development of irrigation area and the reasonable utilization of regional water resources.关键词
地下水位/主成分分析/多变量时间序列/预测Key words
groundwater/principal component analysis/multivariate time series/forecast分类
建筑与水利引用本文复制引用
张展羽,梁振华,冯宝平,黄继文,吴东..基于主成分-时间序列模型的地下水位预测[J].水科学进展,2017,28(3):415-420,6.基金项目
国家自然科学基金资助项目(51179050) （51179050）
山东省水生态文明试点科技支撑计划(ZC201450519)The study is financially supported by the National Natural Science Foundation of China (No.51179050). （ZC201450519）
