Metadata-Version: 2.1
Name: pylenm
Version: 0.1.6
Summary: 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.
Home-page: https://github.com/AurelienMeray/pylenm
Author: Aurelien Meray
Author-email: aurelien.meray@gmail.com
License: MIT
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/
        
        # Demo/Guide
        Demo notebook can be found in the demo folder.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
