京津冀平原浅层地下水漏斗演变规律与影响因素  

Evolution and influence factors of shallow groundwater depression cone in Beijing-Tianjin-Hebei Plain

作　　者：南天 曹文庚[1,2] 任印国 孙龙 高媛媛[5] NAN Tian;CAO Wengeng;REN Yinguo;SUN Long;GAO Yuanyuan(The Institute of Hydrogeology and Environmental Geology,CAGS,Shijiazhuang 050061,China;Hebei Cangzhou Groundwater and Land Subsidence National Observation and Research Station,Shijiazhuang 050061,China;Survey and Research Center of Hebei Province,Shijiazhuang 050061,China;Information Center of the Ministry of Water Resources,Beijing 100053,China;Bureau of South to North Water Transfer of Planning,Designing and Management,Ministry of Water Resources,Beijing 100038,China)

机构地区：[1]中国地质科学院水文地质环境地质研究所,石家庄050061 [2]河北沧州平原区地下水与地面沉降国家野外科学观测研究站,石家庄050061 [3]河北省水文勘测研究中心,石家庄050061 [4]水利部信息中心,北京100053 [5]水利部南水北调规划设计管理局,北京100038

出　　处：《南水北调与水利科技（中英文）》2024年第1期110-121,共12页South-to-North Water Transfers and Water Science & Technology

基　　金：国家自然科学基金项目(41972262);河北省自然科学基金优秀青年科学基金项目(D2020504032);地下水位降落漏斗成因及关键控制因子研究(13000022P00329410101J)。

摘　　要：为研究华北地区河湖生态补水对地下水漏斗演变的影响,以京津冀平原浅层地下水漏斗2003年至2022年的相对变化作为识别目标,从气象因素、地形因素、人为因素和含水层水力学特性4个方面进行考虑,选取8个具体指标构建特征变量数据集,使用逻辑回归(logistic regression, LR)、支持向量机(support vector machine, SVM)和随机森林(random forest, RF)方法建立漏斗演变识别模型,并利用敏感度、特异度和决定系数R^(2)对拟合效果进行对比评价,结果显示随机森林为最优模型。进而利用模型分析研究区地下水漏斗演变规律,阐明具体因素对漏斗演变的影响作用。研究表明:京津冀平原区浅层地下水漏斗在2010年之前整体呈扩张趋势,之后在部分地区呈现缩减和消失的态势。河湖补水前,地下水漏斗发展主要受开采影响,其重要度约50%;2018年后河湖补水对抑制漏斗扩张发挥了较为明显的作用,重要度达16%。从发展过程来看,地下水开采依然是控制京津冀平原浅层地下水漏斗变化最重要的因素。对比宁柏隆和高蠡清两个典型浅层地下水漏斗的发展变化可知,河道生态补水对宁柏隆漏斗变化的贡献率接近10%,而对高蠡清漏斗变化影响的重要度仅为1%,因此持续的河流生态补水对宁柏隆漏斗水位恢复会产生积极影响,而对于高蠡清漏斗则需要以水源置换压减农业灌溉地下水量为关键手段实现水位恢复。Since the 1960s,there is continuous groundwater exploitation in the North China Plain.With the rapid increase in water demand,groundwater overexploitation became an environmental geological problem.Recently,restrictions on groundwater exploitation and artificial groundwater recharge were developed to recover the groundwater level and remove the groundwater depression cone in Beijing-Tianjin-Hebei Plain.During the process of river ecological supplement,the recharge source of groundwater would be supplemented,and the water cycle mode could be changed.It is necessary to explain the groundwater depression cone evolution mechanism for accelerating the groundwater level recovery at this stage.Numerical simulation is the traditional method to study the groundwater depression cone variation,but the model operation and construction are relatively complex.With the development of computer science,many machine-learning algorithms are proposed.Because of its simplicity and efficiency,machine learning models are widely used in the hydrogeological research field.Eight specified indicators have been selected to study the variation of groundwater depression cones,considering from natural factors,human activity factors,and hydrology factors.With these indicators,the feature variable data set is formed,and based on the feature variable data set,three typical machine learning models are developed to distinguish the variation of the groundwater depression cone.The logistic regression(LR)model and support vector machine(SVM)model are based on the traditional machine learning algorithm,and random forest(RF)model is a kind of ensemble algorithm based on the tree models.The established models were evaluated by sensitivity,specificity,and R2 accuracy.The feature variable importance and shapely value were produced to quantify the contribution of each indicator to the groundwater depression cone and explain the behavior of each indicator.The results showed that the RF model outperforms the LR and SVM models in terms of model performance.The

关 键 词：京津冀平原 地下水降落漏斗 多源数据驱动模型 机器学习 演化机制 

分 类 号：TV211[水利工程—水文学及水资源]