Modeling groundwater level using geographically weighted regression

An economic development, crop production, and socioeconomic development highly dependent on the availability of groundwater resources in nearby areas. In order to manage groundwater sustainably, it is crucial to predict groundwater levels. Analysis of groundwater levels along with various influential factors becomes possible due to the availability of remotely sensed geospatial data. The spatially differing groundwater level is highly influenced by the geographical factors called influential factors as like elevation and slope. In the present study, we use the spatial regression and geographically weighted regression (GWR) models for predicting the groundwater level. The GWR model gives comparatively satisfactory results as compared to the three variants of the spatial regression models with lower Bayesian information criterion value (1101.04) and highest \(R^2\) value (0.84). It can be noted that the factors of vegetation index, drought index, elevation, and topographic position positively affect the groundwater level. While the factors of roughness, surface temperature, precipitation, and runoff are affected negatively. The current study highlights that GWR model is useful for exploring the spatial relationships between the different influencing factors and the groundwater level.

Prediction groundwater level using geographically weighted regression