Abstract
This study presents a comprehensive analysis of groundwater level prediction and management using an extreme gradient boosting (XGB) model, optimized through Bayesian techniques. To address the challenge of unavailable accurate pumping volume data in high-density agricultural well areas, our approach leverages well power consumption as a key feature for the machine learning model. This innovative method enables accurate groundwater level predictions based on precipitation and power consumption data. To mitigate significant groundwater level declines during drought periods, the developed XGB model offers flexible design scenarios with varying degrees of groundwater extraction reduction. This capability allows for rapid predictions of groundwater levels, providing decision-makers with a powerful tool to adapt to hydrological uncertainties caused by future climate change. The results of model testing present that the increases in groundwater levels with a 25% reduction in power consumption range from 0.45 to 0.79 m during the wet season and from 0.45 to 0.99 m during the dry season. Interestingly, as the percentage of power consumption reduction increases, the elevations in groundwater levels do not increase proportionally, indicating that the non-linear characteristics among the interactions of precipitation, pumping behaviors, and groundwater level variations. In all three scenarios, the increases in groundwater levels during the dry season are significantly greater than those during the wet season. This implies that appropriate reductions in pumping volumes during drought periods can effectively prevent sharp groundwater level drawdowns. Furthermore, the XGB model plays a crucial role in formulating groundwater extraction reduction policies and agricultural fallow subsidy programs. When considering the opportunity cost of agricultural labor, the subsidies for the first and second crop periods meet only 30% and 59% of the economic profit, respectively. This economic shortfall is a major barrier to the adoption of fallowing practices by farmers during droughts. Therefore, it is crucial to enhance these subsidies to make fallowing a more viable and attractive option for farmers. In conclusion, while predictive modeling offers a robust tool for groundwater management and policy decision-making, there is a clear need for improved economic incentives and integrated management strategies.