Boris-Droz / PRODOM

Tools for analysing fluorescence data using PARAFAC and Machine Learning (ML)

PRODOM

Tools for analysing fluorescence data using PARAFAC and Machine Learning (ML)

Project: PRODOM - Proactive Optical Monitoring of Catchment Dissolved Organic Matter for Drinking Water Source Protection

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Description

EEM-PARAFAC follow the tutorial of Pucher, M.; et al. Water 2019, 11 (11), 2366. https://doi.org/10.3390/w11112366 Then the ML tools are follow different package in R for quantitative calibration and model evaluation. Description are given in the beggining of each scripts.

Implementation notes

Source code is primarily written in the R language version 4.2.0.

Getting started

1. Tutorialv1.0 pdf file contain a small description on the script and procedure to follow.
2. Copy the folder EEM-PARAFAC and ML-script who contain script
3. Check out folder input for exemple of input
4. Follow the instructions on each description of annoted script
5. Enjoy your predictions ...

Associated data set and workflow

Droz, B.; Fernández-Pascual, E.; O’Dwyer, J.; Goslan, E. H.; Quishi, X.; Harrison, S.; O’Driscoll, C.; Weatherill, J. Data of the EPA - PRODOM project v1.0. https://doi.org/10.5281/zenodo.7244913.

Droz, B.; Fernández-Pascual, E.; O’Dwyer, J.; Goslan, E. H.; Quishi, X.; Harrison, S.; O’Driscoll, C.; Weatherill, J. Calibrated machine learning model workflow for disinfection byproduct formation prediction for R. v1.0. https://doi.org/10.5281/zenodo.7886046.