Spatial Modeling of Groundwater Potential Existence Using GIS: Nineveh Governorate as a Model

Geographic Information System (GIS)-based studies have gained substantial importance. This is due to their remarkable efficiency in processing spatial information, thereby providing direct information about the resource is necessary that can be utilized for investment optimization. The issue of the study is stemmed from a fundamental question: "Does spatial modeling using Geographic Information Systems (GIS) play a role in identifying potential areas for groundwater replenishment?". In light of this, the study has formulated the hypothesis that spatial modeling using Geographic Information Systems (GIS) supported by Multi-Criteria Decision Analysis (MCDA) techniques, plays a significant role in identifying potential areas for groundwater replenishment. The study aims to identify these potential areas and elucidate how Geographic Information Systems technology can be leveraged in this context. The use of this technology opens up a new avenue in the study of water resources, enabling the production of maps that depict potential groundwater replenishment areas. To achieve the study's objectives and test the hypothesis, a set of factors influencing the probability of groundwater replenishment is modeled using data from various sources. The Analytic Hierarchy Process (AHP) technique is employed to generate weights for these factors, and a specialized tool for modeling the probability of groundwater replenishment, named "Groundwater Potential Existence," is developed. This tool automates map generation based on the weights derived from hierarchical analysis. The modeled map using the mentioned technique reveales that the probability of groundwater replenishment decreases in the southern and southwestern regions of Nineveh Province and gradually increases in a northerly and northeasterly direction within the province. Despite its elevated and rugged terrain, the gradual increase in rainfall rates towards the north, the proximity to water bodies, as well as higher vegetation cover and lower evaporation rates in these areas, had the most significant impact on elevating the likelihood of groundwater replenishment in these regions. The study further progresses to conduct a sensitivity analysis of the groundwater replenishment probability model to identify which factors had the most significant influence on the model, using Pearson correlation coefficients. Subsequently, the most sensitive factors are incorporated into a multiple regression analysis to derive an optimized predictive model in case the values of these five factors changed. A second Pearson correlation is performed to validate the optimized model revealing a strong correlation of (0.8) between the optimization model and the groundwater probability model indicating a robust connection and making it suitable for predictive purposes