biostudies-literatureMeray AOBiological and Environmental ResearchOffice of Environmental ManagementLawrence Berkeley National Laboratory5973-5983https://www.ebi.ac.uk/biostudies/studies/S-EPMC9069689biostudies-literatureUnknown56(9)In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regressioEnvironmental science & technologyPyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies.PMC9069689DE-EM0005213DE-AC02-05CH11231Sturla SGonzalez-Raymat HDenham MWainwright HMMeray AOLagos LESerata RUpadhyay HUhlemann SEddy-Dilek CSiddiquee MRfalsePyLEnM: 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 contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regressio2022-01-01T00:00:00Z2022 May2025-04-19T09:19:10.396Z2025-04-19T09:19:10.396ZS-EPMC90696893542713310.1021/acs.est.1c07440