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Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard

Luis Otávio Miranda Peixoto1, Jorge Luis Gabriel Ferreira da Silva da Costa Pereira2, Cristovão Vicente Scapulatempo Fernandes3

  • 1Departament of Hydraulics and Sanitation, Universidade Federal Do Paraná, Curitiba, Brazil. luisotaviopeixoto@gmail.com.

Environmental Monitoring and Assessment
|December 5, 2024
PubMed
Summary

This study predicts caffeine contamination in water using machine learning models. Ensemble-RF proved most effective for estimating caffeine levels, offering a tool for rapid water quality assessment.

Keywords:
Caffeine contaminatiosEnsemble artificial intelligence methodsEnvironmental water modeling

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Analytical Chemistry

Background:

  • Caffeine is an emerging contaminant and a reliable indicator of human impact on water bodies.
  • Assessing water contamination levels is crucial for environmental protection and public health.

Purpose of the Study:

  • To develop and evaluate predictive models for caffeine concentration in water.
  • To identify the most effective machine learning techniques for predicting caffeine contamination using common water quality parameters.

Main Methods:

  • Utilized Artificial Neural Networks (ANN) and Random Forest (RF) modeling.
  • Employed hybrid and ensemble methods for both regression and classification tasks.
  • Validated model performance using readily available water quality data.

Main Results:

  • Ensemble-RF demonstrated superior performance in estimating caffeine concentrations (regression).
  • Ensemble-RF, ANN, and Ensemble-ANN showed promise for classifying contamination levels.
  • The models successfully predicted caffeine contamination using accessible water quality parameters.

Conclusions:

  • Machine learning models, particularly ensemble methods, can effectively predict caffeine contamination in water resources.
  • This approach provides a valuable and rapid tool for water quality assessment and management.
  • Findings support proactive strategies for safeguarding water systems from emerging contaminants.