In recent decades, the growing reliance on groundwater for irrigation and domestic purposes has underscored the need for precise monitoring of groundwater levels. Conventional groundwater level mapping methods, such as manual contouring and calibrated numerical models, encounter challenges in terms of time, complexity, scale, and cost. To overcome these limitations, we propose to develop a novel framework that integrates multiple datasets using physics-informed space-time geostatistical methods to enhance the precision of groundwater level estimation. Our approach will combine diverse data sources, including remote sensing data, hydrogeological surveys, and historical groundwater measurements, to create a comprehensive understanding of aquifer systems.
This research will contribute to advancing of physics-informed space-time geostatistical methodologies, setting the stage for future directions, including method refinement and application in hydrogeology. Our integrated approach will provide a robust solution for assessing and managing groundwater resources in data-scarce regions.
This research focuses on developing and adapting methodologies for physics-guided geostatistics, machine learning, and data fusion. The primary objective is to provide an effective solution to the challenges of managing groundwater in regions with limited data, such as various areas in Pakistan and globally.
