Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Abstract

Introduction: Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. Methods: The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO 2 and NO x concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NO x and NO 2 concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Results: Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R 2 ) values are 0.92 and 0/94 for NO 2 and NO x concentrations, respectively. But in MLR model, R 2 values were 0.41 and 0.44 for NO 2 and NO x concentrations, respectively. Conclusions: This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO 2 and NO x concentrations in an urban environment compared to linear models.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

“…The best structure for the MLR model RMSE and R 2 were 3.6 and 0.42 respectively in comparison to the MLP model with RMSE = 0.0046 and R 2 = 0.82. The findings in this study shows that performance of MLP is superior in comparison to MLR for prediction NO2 concentrations in urban environments 39 . Cabaneros et al illustrated that MLP models can accurately forecast NO 2 concentrations (R 2 = 0.9, RMSE = 23.45) based on air pollution and meteorological data 19 .…”

“…This research was used, as input, daily values of air temperature, humidity, www.nature.com/scientificreports/ The findings for each five air pollutants highlighted that neural network models to be significantly superior compared to MLR owing to their ability to effectively predict the complex and nonlinear issues such as air pollution 42 . Although implementation of regression methods is simple, however, the results of various studies also exhibit that regression methods may not offer precise predictions in some areas such as air pollution in comparison to ANNs models; and ANNs have generally superior performance 39,43,44 .The comprehensive data collection including green space and traffic data is the most advantages of our study. As a result, our ANN model is more accurate than other developed models in research.…”

“…Artificial Neural Network (ANN) models could be used as interpolation methods for complex and non-linear problems such as predicting and modeling air pollution 17 , 35 , 36 . Several research exhibits the performance of ANNs and traditional regression models to predict air pollutant concentrations 19 , 37 – 39 . Dragomir et al compared the multiple linear regression and multilayer perceptron for the forecast of the NO 2 concentration in Romania.…”

Machine Learning‐Enabled Smart Sensor Systems

“…[108] Forecasting and modeling the NO X gas concentrations is crucial to understanding and deploying preventative measures to mitigate the pollutants. An NN system reported by Rahimi [83] predicts the NO X concentration found in Tabriz, Iran, from meteorological (wind speed, wind direction, precipitation, vapor pressure, barometric pressure, temperature, humidity, and radiation). NO X and O 3 gas data are used to train the NN model.…”

A review of artificial neural network models for ambient air pollution prediction

“…As poor air quality in urban areas has been attributed to chronic diseases and premature mortalities of vulnerable members of the public (Organisation for Economic Cooperation and Development, 2016;World Health Organization, 2016), a greater demand is directed towards policy-makers and urban city planners to provide rapid and parsimonious solutions to circumvent the effects of air pollution (Baklanov et al, 2007;Moustris et al, 2010). In recent years, ANNs have been successfully implemented in many short-and long-term forecasting applications (Biancofiore et al, 2017a;Cabaneros et al, 2017;Coman et al, 2008;Ibarra-Berastegi et al, 2008;Lightstone et al, 2017;Rahimi, 2017). Furthermore, more practitioners resort to data-driven approaches such as ANNs as alternatives to traditional deterministic or physics-based approaches, e.g.…”