Forecasting Groundwater Depletion in Quetta Sub-Basin using Remote Sensing Data: Deep Learning Approach

Groundwater resource is the largest freshwater storage resource, and it is also the largest storage for most human usage via domestic, industrial, and agricultural water supply and has maintained global food and water security. Therefore, it’s important to sustain the supply and demand to balance the huge population. Pakistan is one of the largest groundwater consumers in the region of South Asia, which extracts roughly 60 billion m
³
, and Pakistan’s groundwater table is dropping a meter per year. Groundwater has been over-exploited for drinking and agriculture. Balochistan is the country’s largest province by size but the smallest by population; the situation is worse than in other regions of the country because groundwater level (GWL) is declining at a pace of 2 to 3 m per year. The primary goal of this research is to anticipate GWL changes and depletion, as well as to evaluate various machine learning techniques in predicting the best deep learning models for forecasting GWL changes utilizing GRACE and GLDAS data in the Quetta District of Balochistan. Deep learning models are used to predict aquifers’ water-balancing capacities. The proposed approach would use data modeling with groundwater levels and meteorological factors as training parameters. By combining advanced groundwater modeling techniques and various datasets, the study aims to provide insights and implications for the entire research area. The ultimate goal is to develop effective management measures and ensure a sustainable supply of groundwater to support the needs of the large population in Balochistan, Pakistan, and the region of South Asia as a whole.