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

Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hybrid and ensemble methods, to predict caffeine con...

Full description

Saved in:
Bibliographic Details
Published in:Environmental monitoring and assessment 2024-12, Vol.197 (1), p.30
Main Authors: Peixoto, Luis Otávio Miranda, da Costa Pereira, Jorge Luis Gabriel Ferreira da Silva, Fernandes, Cristovão Vicente Scapulatempo, Centeno, Jorge Antonio Silva, de Azevedo, Júlio César Rodrigues
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Caffeine
Caffeine - analysis
Classification
Contaminants
Contamination
Decision trees
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental Monitoring - methods
Modelling
Models, Chemical
Monitoring/Environmental Analysis
Neural networks
Neural Networks, Computer
Pollutants
Rivers
Sanitation
Statistical analysis
Statistical methods
Statistical models
Support vector machines
Surface water
Water Pollutants, Chemical - analysis
Water pollution
Water Pollution, Chemical - statistics & numerical data
Water quality
Water resources
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hybrid and ensemble methods, to predict caffeine concentrations (in regression and classification scenarios) using readily available water quality parameters. The results indicate Ensemble-RF as the most effective method for estimating caffeine concentrations, while classification scenarios highlight Ensemble-RF, ANN, and Ensemble-ANN as promising methodologies for predicting contamination levels. This study offers a valuable tool for swiftly assessing caffeine contamination in water, leveraging easily obtainable data, with implications for safeguarding water resource systems.
ISSN:0167-6369
1573-2959
1573-2959
DOI:10.1007/s10661-024-13423-2