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/
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
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
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)
Contributors
Aurelien Meray
Haruko Wainwright
Himanshu Upadhyay
Masudur Siddiquee
Savannah Sturla
Nivedita Patel
Kay Whiteaker
Haokai Zhao
Maintainers
Haokai Zhao
Haruko Wainwright
Release files for pylenm 2.7.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pylenm-2.7.5.tar.gz | 95.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pylenm-2.7.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 200.1 kB
Release files / pylenm-2.7.5.tar.gz
| Download URL | pylenm-2.7.5.tar.gz |
|---|---|
| Size | 95.1 kB |
| Tags | Source |
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| Download URL | pylenm-2.7.5-py3-none-any.whl |
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| Tags | Python 3 |
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