The significant increase in urbanization has resulted in greater use of the subsurface in urban planning and, therefore, increased interaction between groundwater and underground infrastructure. Numerical models are the primary tool adopted to manage the resulting problems; however, their construction is time- and cost-consuming. Groundwater-level time-series analysis can be a complementary method, as this data-driven approach does not require an extensive understanding of the geological and boundary conditions, even if providing insights into the hydrogeologic behaviour. Thus, a data-driven approach was adopted to analyse groundwater time-series of the shallow aquifer, occupied by several underground structures, beneath Milan city (Northern Italy). Statistical (Mann-Kendall and Sen’s slope estimator, autocorrelation and cross-correlation, hierarchical cluster analysis) and geospatial techniques were used to detect the potential variables influencing the groundwater levels of 95 monitoring wells, covering the period 2005–2019. A general rising trend of the water table was identified, with local hydrogeologic differences in the western and southernmost areas. Based on time-series analysis results, four management areas have been identified. These areas could act as future geographic units with specific groundwater management strategies. In particular, subsurface public car parks can be classified with respect to groundwater flooding as (1) not submerged, (2) possibly critical, or (3) submerged at different groundwater conditions. According to these outcomes, targeted guidelines for constructing new car parks have been elaborated for each management area. The methodology proved to be efficient in improving the urban conceptual model and helping stakeholders design the planned underground development, considering groundwater aspects.

城市化的显着增加导致在城市规划中更多地使用地下，因此增加了地下水和地下基础设施之间的相互作用。数值模型是用于管理由此产生的问题的主要工具；然而，它们的建造既费时又费钱。地下水位时间序列分析可以是一种补充方法，因为这种数据驱动的方法不需要对地质和边界条件有广泛的了解，即使提供了对水文地质行为的见解。因此，采用数据驱动的方法来分析米兰市（意大利北部）下方由多个地下结构占据的浅层含水层的地下水时间序列。统计（Mann-Kendall 和 Sen 的斜率估计，自相关和互相关，层次聚类分析）和地理空间技术用于检测影响 2005-2019 年期间 95 口监测井地下水位的潜在变量。确定了地下水位总体上升趋势，西部和最南部地区存在局部水文地质差异。根据时间序列分析结果，确定了四个管理领域。这些地区可以作为具有特定地下水管理战略的未来地理单元。特别是，地下公共停车场可以根据地下水淹没分类为（1）未淹没，（2）可能很严重，或（3）在不同的地下水条件下淹没。根据这些成果，为每个管理区域制定了建设新停车场的有针对性的指导方针。