Data-driven decision management of urban underground infrastructure through groundwater-level time-series cluster analysis: the case of Milan (Italy) | Gestion décisionnelle basée sur les données relatives aux infrastructures souterraines urbaines par l’analyse typologique des séries chronologiques des eaux souterraines: le cas de Milan (Italie) Manejo de decisiones basadas en datos de la infraestructura subterránea urbana a través del análisis de clústeres de series temporales de niveles de agua subterránea: el caso de Milán (Italia) 基于地下水位时间序列聚类分析的城市地下基础设施的数据驱动决策管理:以米兰(意大利)为例 Gestione data-driven a supporto delle decisioni per le infrastrutture sotterranee urbane attraverso la cluster analysis di serie storiche dei livelli piezometrici: il caso di Milano (Italia) Gerenciamento de decisão orientada por dados da infraestrutura subterrânea urbana por meio de análise de agrupamento de séries temporais de nível das águas subterrâneas: o caso de Milão (Itália)

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.