Smart Groundwater Recharge Management using Cloud Computing and Gradient Boosting Machines

This research presents a new method for managing groundwater recharge that uses gradient boosting machines (GBM) and cloud computing. In dry and semi-arid areas where water is scarce, groundwater recharge is vital for maintaining water supplies. Adaptability and real-time reactivity to changing environmental circumstances are sometimes lacking in traditional recharge management systems. To improve groundwater recharging techniques, provide a cloud-enabled system that combines GBM models with real-time data streams. The GBM framework is used to make recharge choices and anticipate groundwater levels. It is famous for its strong prediction powers. Faster model iteration and fast processing of massive data sets are made possible by the scalability improvements made possible by cloud computing infrastructure. Compared with more traditional methods, the proposed system's recharge management efficiency and flexibility are noticeably higher. The technique has proven useful in improving groundwater recharge under different environmental situations, as shown in case studies and simulations. This research addresses inefficient groundwater recharge prediction utilizing cloud computing and GBM, resulting in increased accuracy, real-time monitoring, and optimized sustainable water management practices.