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 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,...
| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2023
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| Online Access: | https://hdl.handle.net/1721.1/147625 |
| _version_ | 1811098318019756032 |
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| author | Meray, Aurelien O Sturla, Savannah Siddiquee, Masudur R Serata, Rebecca Uhlemann, Sebastian Gonzalez-Raymat, Hansell Denham, Miles Upadhyay, Himanshu Lagos, Leonel E Eddy-Dilek, Carol Wainwright, Haruko M |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Meray, Aurelien O Sturla, Savannah Siddiquee, Masudur R Serata, Rebecca Uhlemann, Sebastian Gonzalez-Raymat, Hansell Denham, Miles Upadhyay, Himanshu Lagos, Leonel E Eddy-Dilek, Carol Wainwright, Haruko M |
| author_sort | Meray, Aurelien O |
| collection | MIT |
| description | 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 regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. 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. |
| first_indexed | 2024-09-23T17:12:51Z |
| format | Article |
| id | mit-1721.1/147625 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:12:51Z |
| publishDate | 2023 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1476252023-01-21T03:00:50Z PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies Meray, Aurelien O Sturla, Savannah Siddiquee, Masudur R Serata, Rebecca Uhlemann, Sebastian Gonzalez-Raymat, Hansell Denham, Miles Upadhyay, Himanshu Lagos, Leonel E Eddy-Dilek, Carol Wainwright, Haruko M Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 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 regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. 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. 2023-01-20T19:56:50Z 2023-01-20T19:56:50Z 2022-05-03 2023-01-20T19:52:56Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147625 Meray, Aurelien O, Sturla, Savannah, Siddiquee, Masudur R, Serata, Rebecca, Uhlemann, Sebastian et al. 2022. "PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies." Environmental Science & Technology, 56 (9). en 10.1021/acs.est.1c07440 Environmental Science & Technology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Meray, Aurelien O Sturla, Savannah Siddiquee, Masudur R Serata, Rebecca Uhlemann, Sebastian Gonzalez-Raymat, Hansell Denham, Miles Upadhyay, Himanshu Lagos, Leonel E Eddy-Dilek, Carol Wainwright, Haruko M PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title | PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title_full | PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title_fullStr | PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title_full_unstemmed | PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title_short | PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies |
| title_sort | pylenm a machine learning framework for long term groundwater contamination monitoring strategies |
| url | https://hdl.handle.net/1721.1/147625 |
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