Algorithms Air Quality Estimation: a Comparative Study of Stochastic and Heuristic Predictive Models

• Sánchez-Pozo, Nadia N. ^{[3] [5]} ; Sergi Trilles-Oliver ^{[2] [3]} ; Albert Solé-Ribalta ^[3] ; Lorente-Leyva, Leandro L. ^[5] ; Dagoberto Mayorca-Torres ^{[1] [5] [4]} ; Peluffo-Ordóñez, Diego H. ^{[5] [6]}
  1. [1] Universidad de Granada

     Universidad de Granada

     Granada, España

     
  2. [2] Universitat Jaume I

     Universitat Jaume I

     Castellón, España

     
  3. [3] Universitat Oberta de Catalunya

     Universitat Oberta de Catalunya

     Barcelona, España

     
  4. [4] Universidad Mariana

     Universidad Mariana

     Colombia

     
  5. [5] SDAS Research Group (Ibarra, Ecuador)
  6. [6] Mohammed VI Polytechnic University. Modeling, Simulation and Data Analysis (MSDA) Research Program (Ben Guerir, Morocco)

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• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López , Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 293-304
• Idioma: inglés
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• Resumen

  • 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.