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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.

Project description

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 the new paradigm of long-term monitoring based on state-of-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. In particular, we focus on (1) spatially integrative technologies for monitoring system vulnerabilities – surface cap systems and groundwater/surface water interfaces, and (2) in situ monitoring technologies for monitoring master variables that control or are associated with contaminant plume mobility and direction. This system transforms the monitoring paradigm from reactive monitoring – respond after plume anomalies are detected – to proactive monitoring – detect the changes associated with the plume mobility before concentration anomalies occur.

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/

Demonstration notebooks:

1 – Basics
2 - Unsupervised learning
3 – Water Table estimation & Well Optimization

Demonstration data:

The data used in the demonstration notebooks above can be downloaded here.

Contributors:

Aurelien Meray
Haruko Wainwright
Himanshu Upadhyay
Masudur Siddiquee
Savannah Sturla
Nivedita Patel

Project details


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