Abstract: Soil and groundwater contamination migration are forecasted according to instructions stored in a memory and executable by a processor to facilitate prompt and accurate remediation efforts. In embodiments, an environmental machine learning model is employed, and analysis and determination of contaminant plume distances, sources and destinations are made. A database stores raw environmental site data, from which relevant data can be extracted for a site of interest, and the environmental machine learning model can be trained on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest.

Abstract: Soil and groundwater contamination migration are forecasted according to instructions stored in a memory and executable by a processor to facilitate prompt and accurate remediation efforts. In embodiments, an environmental machine learning model is employed, and analysis and determination of contaminant plume distances, sources and destinations are made. A database stores raw environmental site data, from which relevant data can be extracted for a site of interest, and the environmental machine learning model can be trained on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest.