Remote Sensing-Driven Prediction of Groundwater Nitrate Risk: Insights from Machine Learning Applications in Taiwan

Groundwater nitrate (NO₃^-) pollution is a pressing issue linked to agricultural practices, urbanization, and industrial activities. This study focuses on Taiwan’s groundwater nitrate nitrogen (NO₃-N) contamination by integrating satellite remote sensing, groundwater monitoring, and various environmental factors using GIS. Data from 451 monitoring stations, sampled quarterly from 2020 to 2024, reveal that NO₃-N concentrations generally range between 1–10 mg/L, while approximately 2% exceed Taiwan’s Drinking Water Quality Standards of 10 mg/L for NO₃-N (equivalent to 44.3 mg/L NO₃^-). In this study, machine learning models, including Random Forest (RF), Multilayer Perceptron, and Support Vector Classifier, were employed to predict NO₃-N contamination risk at three ranges of concentrations (<1, 1–10, >10 mg/L) using different feature combinations: (1) all features, (2) selective environmental factors, and (3) vegetation indices (VIs) alone. RF demonstrated the highest overall accuracy across all combinations, achieving 87% in Feature Combination I. For Feature Combination III, which only used VIs derived from remote sensing, RF achieved an OA of 68%, highlighting its potential for practical and efficient application without ground-based survey data. Key findings highlight the pivotal role of environmental variables, including VIs derived from Sentinel-2 multispectral imagery, terrain parameters from digital elevation models, and meteorological data in mapping contamination hotspots. Future work should integrate higher-resolution satellite imagery and more advanced parameters to improve model performance and decision-making accuracy.