Bridging Data Gaps in Water Quality Modelling: A Machine Learning Framework for Absence Point Generation in Geospatial Binary Classifications

Geospatial monitoring of water quality is essential for managing and protecting groundwater resources, particularly in agricultural regions where nitrate contamination poses significant environmental and public health risks. This study presents a novel methodology for generating absence points in geospatial binary classifications applied to nitrate levels in groundwater across Odense, Denmark. We developed machine learning designed to generate absence points using multiple approaches for binary classification: random, buffer-based, similarity-based, and Maxent-based methods. The integration of maximum entropy into the absence generation workflow allowed us to identify low-susceptibility zones, improving the accuracy of binary classification. The dataset comprised geospatial nitrate concentration levels derived from environmental, hydrological, and anthropogenic variables. Spatial data included high-resolution land-use maps and hydrological parameters. Model evaluation was conducted using Random Forest, with results indicating that the Maxent-based approach consistently outperformed other methods across all metrics, including precision (0.96), AUC (0.96), and TSS (0.91). This method proved particularly effective in handling the challenges associated with presence-only data and produced the most reliable predictions for nitrate contamination in groundwater. The findings underscore the importance of leveraging advanced absence generation techniques to enhance model performance in geospatial classification modeling.