Assessing the impact of subsurface conditions and aging infrastructure on urban land subsidence

Land subsidence is a critical issue in urban coastal areas, driven by both natural geological processes and human activities such as groundwater extraction, infrastructure degradation, and urbanization. This study examines land subsidence patterns in Hoboken, New Jersey, using an integrated modeling framework that combines the Land Subsidence Severity Index (LSSI) and the Risk of Infiltration Index (RI), the latter focusing on sewer network deterioration. A multi-criteria analysis employing the Analytical Hierarchy Process (AHP) was used to assess the relative importance of hydrogeological variables, while a weighted overlay analysis enabled the integration of LSSI and RI layers for predictive subsidence mapping.

Sentinel-1 SAR data were processed using the Small Baseline Subset (SBAS) technique to derive InSAR-based subsidence rates at spatial resolutions of 20 m, 40 m, and 80 m. Nine LSSI-RI weight combinations were tested and evaluated using precision and recall metrics across four subsidence severity levels. The optimal model, assigning 70 % weight to LSSI and 30 % to RI, achieved 96.00 % precision and 51.49 % recall in the very high severity zone, which significantly outperform lower LSSI-weighted configurations. This result underscores the importance of hydrogeological conditions in severe subsidence prediction and highlights the value of integrating satellite remote sensing with infrastructure and geotechnical data to enhance urban risk assessment. The findings provide a transferable framework to support proactive urban planning, infrastructure maintenance, and subsidence risk mitigation, which is particularly important in vulnerable coastal cities facing aging underground infrastructure and shallow groundwater conditions.