PREDICTING GROUNDWATER LEVELS IN THE PIEDMONT PROVINCE USING STATISTICAL MODEL

Mapping groundwater levels is crucial for sustainable water resource management, environmental protection, and structural damage prevention. Contaminants from degraded sewer pipes and high groundwater levels pose significant health risks and structural challenges. Additionally, groundwater levels influence the feasibility of stormwater infrastructure and the efficiency of wastewater treatment plants (WWTPs). Therefore, it is essential to map groundwater levels and identify points where they are vulnerable to contamination or infiltration into sewer systems.

The overarching objective of this research is to develop a repeatable statistical interpolation technique to infer depth to groundwater in urban and suburban areas across the Piedmont physiographic province. Methodologically, we employ multiple linear regression models incorporating variables such as relative elevation, distance to the nearest waterbody, and the maximum distance to either a stream channel or waterbody.

Our initial results show that relative elevation correlates with groundwater depth at 0.7369. The distance to the nearest waterbody shows a positive correlation of 0.4998, while the maximum distance to either a stream channel or waterbody yields a correlation of 0.4473. These correlations indicate that these variables are significant predictors of groundwater depth.

Future work will focus on developing a comprehensive multiple linear regression model using these parameters to predict groundwater levels across the Piedmont. This model aims to provide a robust tool for water resource management and environmental protection.

Keywords:

Groundwater mapping, Statistical interpolation, Environmental protection, Water resource management