PREDICTIVE ANALYTICS IN ENVIRONMENTAL GEOCHEMISTRY: MACHINE LEARNING AND MATHEMATICAL MODELS FOR POLLUTION SOURCE ATTRIBUTION

Abstract

 Environmental geochemistry has evolved as a crucial discipline for understanding pollutant behaviour in soil, water, and air systems. However, accurately tracing the origin and movement of contaminants remains a persistent challenge. This study presents an integrated framework of predictive analytics and machine learning for pollution source attribution within geochemical environments. By combining spatial geochemical datasets with mathematical models such as multivariate regression, random forest classification, and support vector machines, the research aims to forecast pollutant concentration gradients and identify dominant emission sources with high precision. The model leverages parameters such as pH, redox potential, heavy metal concentration, and organic content to establish non-linear correlations between pollution signatures and their probable anthropogenic or natural origins. GIS-based geostatistical mapping supports spatial validation of the predictive outputs, revealing pollution hotspots and diffusion patterns across heterogeneous terrains. The proposed methodology bridges environmental data science and geochemical modelling, offering a scalable, data-driven solution for proactive environmental monitoring and remediation planning. The findings are expected to enhance policy formulation, optimize mitigation strategies, and contribute to sustainable management of contaminated ecosystems.