Machine Learning Approach for Groundwater Contamination Spatiotemporal Relationship Exploration

Abstract

Addressing groundwater contamination, this study applies machine learning (ML) algorithms to explore the spatiotemporal dynamics of hexavalent chromium (Cr[VI]) at the Hanford 100-Area. The research uses an extensive long-term monitoring dataset focused on groundwater wells and aquifers to enhance the understanding and management strategies of this complex environmental issue and predict the impact on aquifers due to the contamination in groundwater wells. The challenging nature of the task is due to various factors, such as the geological nature of the soil, pipeline leaks, and mobility of the particles that impact the speed of contamination. The findings demonstrate a random forest (ML)-based approach to predict the contaminant distributions accurately, thus significantly reducing uncertainties in contamination assessments and refining conceptual site models. This approach advances groundwater quality management and sets a precedent for future AI-driven environmental studies.