Abstract
This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.
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Acknowledgment
This work is supported by the SDAS Research Group (www.sdas-group.com). Authors are in debt with the SDAS Group internal editor J. Mejía-Ordóñez for the manuscript reviewing and editing.
Sergio Trilles has been funded by the Juan de la Cierva - Incorporación postdoctoral programme of the Ministry of Science and Innovation - Spanish government (IJC2018–035017-I).
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Sánchez-Pozo, N.N., Trilles-Oliver, S., Solé-Ribalta, A., Lorente-Leyva, L.L., Mayorca-Torres, D., Peluffo-Ordóñez, D.H. (2021). Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Models. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_25
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DOI: https://doi.org/10.1007/978-3-030-86271-8_25
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