PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies

In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contaminationmonitoring as the Python package PyLEnM (Python for Long-termEnvironmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance andquality control (QA/QC), coincident/colocated data identification, theautomated ingestion and processing of publicly available spatial data layers,and novel data summarization/visualization. The key ML innovations include(1) time series/multianalyte clustering tofind the well groups that havesimilar groundwater dynamics and to inform spatial interpolation and welloptimization, (2) the automated model selection and parameter tuning,comparing multiple regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatialdata layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new welloptimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-termmonitoring. We demonstrate our methodology using the monitoring data at the Savannah River Site F-Area. Through this open-source PyLEnM package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relations