Machine Well Screening Method Based on POI Data and DBSCAN Clustering Algorithm

Maintaining machinery wells, key to groundwater sustainability, is vital for managing these precious resources. In keeping with the need for effective groundwater management, this study introduces a screening process leveraging the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and incorporating point of interest (POI) and spatial correlation assessment. Specifically, this approach was utilized in the Fangshan District, Beijing, to detect illegal water extraction wells and bolster water resource management. By integrating POI and land use categorization, we performed a water demand overlay analysis, considering public water provision and well distribution. ArcGIS spatial analysis helped us pinpoint POIs with significant water demand. The DBSCAN clustering algorithm was then employed to scrutinize potential problematic wells. The credibility and practicality of this methodology were confirmed through field studies, meeting current governance standards. The study identified 14 potential unregistered wells, of which 5 were confirmed to be unauthorized, primarily located in Shidu and Zhangfang Towns. Field checks confirmed these findings, highlighting the need for improved groundwater management and validating our method's reliability. Based on these findings, we propose additional steps to enhance groundwater extraction management. This research shows how the use of POI data and the DBSCAN algorithm can aid groundwater resource management in Fangshan District and potentially serve as a model for other regions.