Megha Hegde, Jean-Christophe Nebel, Farzana Rahman

Abstract:

Air pollution exposure poses a major risk to human health, with devastating effects ranging from causing respiratory and cardiovascular diseases, to adverse impacts on cognitive abilities, mental health, and prenatal development. In the case of an excessive build-up of air contaminants, emergency measures must be enacted to reduce human exposure and decrease pollution levels. Hence, cities worldwide have invested in sophisticated air pollution monitoring systems to assess pollution levels and inform public health advice. Predicting spikes in air pollution a few hours in advance is critical in reducing human exposure as much as possible. While deep neural networks have become popular for this task, standard machine learning approaches remain very attractive: they deliver competitive performance without relying on specialised equipment and consume much less energy than their deep learning counterparts. Experiments conducted on London air quality data demonstrate that Linear Regression achieves state-of-the-art performance, with 1-hour and 24-hour predictions displaying 0.2 and 3.2 mean absolute errors respectively. Moreover, its energy usage is a fraction of that of its deep learning competitor, LSTM, consuming over 2000 times less energy for training, and over 100 times less energy for prediction. The results demonstrate that standard machine learning approaches can provide an accurate and energy-efficient approach to air pollution forecasting, without prohibitive hardware investments.

Keywords: Air pollution, Sustainable AI, Machine learning, Energy consumption, Air quality, PM2.5

Date Published: June 6, 2024
DOI: 10.11159/ijepr.2024.003

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