Using machine learning to forecast air and water quality

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.