pylenm 2.7.5

This package aims to provide machine learning (ML) functions for performing comprehensive soil and groundwater data analysis, and for supporting the establishment of effective long-term monitoring.

PyLEnM

This package aims to provide machine learning (ML) functions for performing comprehensive soil and groundwater data analysis, and for supporting the establishment of effective long-term monitoring. The package includes unsupervised ML for identifying the spatiotemporal patterns of contaminant concentrations (e.g., PCA, clustering), and supervised ML for evaluating the ability of estimating contaminant concentrations based on in situ measurable parameters, as well as the effectiveness of well configuration to capture contaminant concentration distributions. Currently, the main focus is to analyze historical groundwater datasets and to extract key information such as plume behaviors and controlling (or proxy) variables for contaminant concentrations (Schmidt et al., 2018). This is setting a ground for integrating new technologies such as in situ sensors, geophysics and remote sensing data.

This development is a part of the Advanced Long-Term Monitoring Systems (ALTEMIS) project. In this project, we propose to establish a new paradigm of long-term monitoring based on state-of-the-art technologies – in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and AI – that will improve effectiveness and robustness, while reducing the overall cost.

The latest package can be downloaded from: https://pypi.org/project/pylenm/

More information on the project can be found here: https://altemis.lbl.gov/ai-for-soil-and-groundwater-contamination/

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Installation

(Optional) Create a virtual environment

conda create --name pylenm_env python=3.8
conda activate pylenm_env

Install PyLEnM

Option 1 — Install from PyPI

pip install pylenm

Option 2 — Install from source

git clone https://github.com/hkzhao7/pylenm.git
cd pylenm
pip install .

Repository root: https://github.com/hkzhao7/pylenm/tree/main

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Journal Publication

PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies
Aurelien O. Meray, Savannah Sturla, Masudur R. Siddiquee, Rebecca Serata, Sebastian Uhlemann, Hansell Gonzalez-Raymat, Miles Denham, Himanshu Upadhyay, Leonel E. Lagos, Carol Eddy-Dilek, and Haruko M. Wainwright
Environmental Science & Technology, 2022, 56 (9), 5973–5983
DOI: https://doi.org/10.1021/acs.est.1c07440

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Demonstration Notebooks

The following notebooks demonstrate the current and most complete version of PyLEnM using sample-based groundwater datasets.
They are the default learning resources for new users.

Run on GitHub (view/download):
https://github.com/hkzhao7/pylenm/tree/main/notebooks

Run on Google Colab (no local setup):

• 1 - Basics
• 2 - Unsupervised learning
• 3 - Water Table Estimation & Well Optimization
• 4 - Tritium Spatial Estimation
• 5 - Proxy Estimation (SC~Tritium)

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Contributors

Aurelien Meray
Haruko Wainwright
Himanshu Upadhyay
Masudur Siddiquee
Savannah Sturla
Nivedita Patel
Kay Whiteaker
Haokai Zhao

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Maintainers

Haokai Zhao
Haruko Wainwright