A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management

Climate-induced extreme hydrological events threaten irrigation water resources and crop production. Groundwater serves as a vital source of irrigation during periods of surface water scarcity; however, excessive and unsustainable abstraction has resulted in land subsidence. While reducing groundwater over-extraction can alleviate this issue, it may also compromise agricultural productivity, particularly during drought conditions. To address this, a reliable assessment tool is needed to balance sustainable groundwater extraction and agricultural productivity. This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. Bayesian optimization was used to determine the best-fit hyperparameters, resulting in RMSE, MAE, and R² values ranging from 0.923 to 2.497 m, 0.709–2.132 m, and 0.057–0.914, respectively, during model validation. Model testing from January 2022 to June 2023 showed a strong correlation between monitored and predicted levels, indicating effective trend capture, despite slight overestimations during the dry seasons. Scenario predictions showed that a 50 % reduction in power consumption for double-crop rice led to groundwater level increases of 0.41–2.31 m in the wet season and 0.54–2.52 m in the dry season, maintaining safe thresholds. However, current fallowing subsidies recover only a fraction of the economic profit from rice production, limiting policy adoption. To improve long-term effectiveness, this study recommends institutionalizing adaptive fallowing policies, such as seasonally adjusted quotas based on real-time groundwater and rainfall indicators, and tiered subsidy schemes according to groundwater risk levels. Embedding these tools within broader agricultural governance frameworks can enhance policy responsiveness and sustainability. The proposed model supports both short-term decision-making and long-term climate-informed groundwater management by balancing environmental protection with food security and economic viability. • Explainable recharge and discharge features used for XGB model development. • Power consumption data utilized to address the lack of pumping volume data. • Model flexibly predicts groundwater levels under varied pumping reduction scenarios. • Supports balancing agricultural productivity with groundwater sustainability. • Emphasizes the role of subsidies in promoting the adoption of fallowing practices.