Abstract
Groundwater quality is a critical concern in agricultural regions, where nitrate contamination poses environmental and health risks, and ammonium levels play a pivotal role in nitrogen cycling processes. This study introduces a multi-task learning (MTL) framework designed to jointly predict nitrate and ammonium levels in groundwater, addressing the interdependencies between these variables. Conducted in Odense, Denmark, the study leverages spatial and temporal data, including hydrological, environmental, and anthropogenic variables, alongside land-use maps. The MTL approach outperforms traditional single-task models by capturing shared environmental and hydrological variables. By sharing information across tasks, the model identifies overlapping spatial, enabling robust predictions even in data-scarce scenarios. Additionally, the shared layers of the MTL model reduce overfitting, improving generalizability and providing deeper insights into the drivers of groundwater quality. The dataset used in this study includes geospatial nitrate and ammonium measurements, which were modeled alongside predictor variables such as land use, soil characteristics, and topographical variables. Model evaluation metrics demonstrated the superiority of the MTL approach, with increased accuracy, R², and reduced root mean squared error (RMSE) compared to separate models. The results highlight the potential of MTL to improve predictions and foster integrated groundwater management strategies. This study underscores the importance of advanced machine learning techniques in environmental modeling, showcasing a novel approach to jointly predict interrelated water quality variables.