Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting

Daniel Silva Campos,Manoel Henrique De Nóbrega Marinho,Hugo Valadares Siqueira,Thiago Antonini Alves,Yara De Souza Tadano

doi:10.4025/actascitechnol.v42i1.48203

Daniel Silva Campos, Manoel Henrique De Nóbrega Marinho + Show 3 more

Open Access

https://doi.org/10.4025/actascitechnol.v42i1.48203

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Abstract

Air pollution is a relevant issue studied worldwide, and its prediction is important for social and economic management. Linear multivariate regression models (LMR) and artificial neural networks (ANN) are widely applied to forecasting concentrations of pollutants. However, unorganized machines are scarcely used. The present investigation proposes the application of unorganized machines (echo state networks - ESN and extreme learning machines - ELM) to forecast hourly concentrations of particulate matter with the aerodynamic diameter up to 10 µm (PM10), carbon monoxide (CO), and ozone (O3) at the metropolitan region of Recife, Pernambuco, Brazil. The results were compared with multilayer perceptron neural network (MLP) and LMR. The prediction was made using or not meteorological variables (wind speed, temperature, and relative humidity) as input data. The results showed that the inclusion of these variables could increase the general performance of the models considering one step ahead forecasting horizons. Also, the ELM and the LMR achieved the best overall results.

Highlights

The results showed that multiple linear regression and quantile regression were the best models to describe PM10 variation
The database was obtained by the Environmental Agency of Pernambuco (CPRH), consisting of concentrations of PM10 [μg.m–3], carbon monoxide (CO) [ppm], and ozone (O3) [μg.m–3], and the meteorological variables relative humidity (RH) [%], average temperature (AT) [°C], and wind speed (WS) [m.s–1], from July 17th, 2015 to July 17th, 2017
We considered the traditional multilayer perceptron (MLP) and two proposals of unorganized machines: extreme learning machine (ELM) and echo state network (ESN)

Summary

Introduction

Air pollution is a widely studied subject mainly due to its impact on human health. It is considered a major environmental risk to public health by the World Health Organization (WHO), which estimates around eight million deaths per year due to the exposure to air pollution (Jasarevic & Lindmeier, 2015). Bad air quality causes chronic diseases and premature mortalities (Cabaneros, Calautit, & Hughes, 2019). In this sense, it is very important to the policy-makers and urban city planners to have previous information about air quality in order to find solutions to avoid or minimize the effects of air pollution on human health

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Conclusion