Real-Time Ground Water Level Detection using Satellite Imagery and XG-Boost Algorithm for Sustainable Water Resource Management

The problem the work solves is for monitoring groundwater levels in peatlands through traditional methods that involve labor-intensive processes, particularly for large areas that cannot be easily accessed. The system applies satellite imagery, the XGBoost algorithm, and MATLAB to extract key environmental features from high-resolution images, which can predict real-time changes in groundwater levels. The proposed alternative solution to the conventional groundwater monitoring techniques is more accurate, scalable, and cost-effective, enabling operational efficiency and cost savings. The main purpose of this system is to help develop sustainable water resource management systems by improving the accuracy of prediction of groundwater levels, thus impacting agricultural planning and conservation initiatives in water-scarce regions. The proposed system offers timely, reliable data that is crucial for better decision-making, reduces dependency on manual methods, and promotes efficient water use in peatland regions.