Using Machine Learning to Forecast Air and Water Quality

Carolina Silva, Bruno Fernandes, Pedro Oliveira, Paulo Novais

Abstract

Environmental sustainability is one of the biggest concerns nowadays. With increasingly latent negative impacts, it is substantiated that future generations may be compromised. The research here presented addresses this topic, focusing on air quality and atmospheric pollution, in particular the Ultraviolet index and Carbon Monoxide air concentration, as well as water issues regarding Wastewater Treatment Plants, in particular the pH of water. A set of Machine Learning regressors and classifiers are conceived, tuned, and evaluated in regard to their ability to forecast several parameters of interest. The experimented models include Decision Trees, Random Forests, Multilayer Perceptrons, and Long Short-Term Memory networks. The obtained results assert the strong ability of LSTMs to forecast air pollutants, with all models presenting similar results when the subject was the pH of water.

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Paper Citation


in Harvard Style

Silva C., Fernandes B., Oliveira P. and Novais P. (2021). Using Machine Learning to Forecast Air and Water Quality.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1210-1217. DOI: 10.5220/0010379312101217


in Bibtex Style

@conference{icaart21,
author={Carolina Silva and Bruno Fernandes and Pedro Oliveira and Paulo Novais},
title={Using Machine Learning to Forecast Air and Water Quality},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1210-1217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010379312101217},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Using Machine Learning to Forecast Air and Water Quality
SN - 978-989-758-484-8
AU - Silva C.
AU - Fernandes B.
AU - Oliveira P.
AU - Novais P.
PY - 2021
SP - 1210
EP - 1217
DO - 10.5220/0010379312101217