PREDICTING AIR QUALITY INDEX BASED ON METEOROLOGICAL DATA: A COMPARISON OF REGRESSION ANALYSIS, ARTIFICIAL NEURAL NETWORKS AND DECISION TREE
AbstractIntroduction: Air pollution can cause health problems on a global scale. Air quality predicting is an effective method to protect public health through early notification hazards of air pollution. The aim of this study is forecasting next day air quality index (AQI) in Tehran, Iran. Materials and methods: Various approaches such as multiple linear regression (MLR) analysis, decision trees (DT), and multi-layer perceptron artificial neural networks (ANN), feature selection with regression analysis before artificial neural networks (MLR-ANN) and feature selection with decision trees before artificial neural networks (DT-ANN) were used for forecasting next day AQI based on meteorological data and one and two days ago AQI. Root mean square error (RMSE) and correlation coefficient (CC) are used to assess models accuracy. Results: The results showed that forecasting of next day AQI by DT-ANN model is more accurate than others. Statistics indexes of this model such as RMSE and CC have been determined as 21.26 and 0.66 respectively. Using of DT for features selection because of reducing the number of inputs and decrease the model complexity was considered better than using the initial data. Conclusions: The applications of air quality forecasting methods could be applied for air quality management purposes and protect public health.
WHO. Global Health Risks, Mortality and burden of
disease attributable to selected major risks. Switzerland:
WHO Library Cataloguing-in-Publication Data;
Adams MD, Kanaroglou PS. Mapping real-time air pollution
health risk for environmental management: Combining
mobile and stationary air pollution monitoring with neural network models. Journal of environmental
management. 2016; 168 (Supplement C):133-41.
Biswanath Bishoi , Amit Prakash , Jain VK. A Comparative
Study of Air Quality Index Based on Factor Analysis
and US-EPA Methods for an Urban Environment
Aerosol and Air Quality Research. 2009; 9 (1):1-17.
Monteiro A, Vieira M, Gama C, Miranda AI. Towards
an improved air quality index. Air Quality, Atmosphere
& Health. 2017; 10 (4): 447-55.
Feng Q, Wu S, Du Y, Xue H, Xiao F, Ban X, et al. Improving
Neural Network Prediction Accuracy for PM10
Individual Air Quality Index Pollution Levels. Environmental
Engineering Science. 2013 Dec 1; 30 (12): 725-
Bai Y, Li Y, Wang X, Xie J, Li C. Air pollutants concentrations
forecasting using back propagation neural
network based on wavelet decomposition with meteorological
conditions. Atmospheric Pollution Research.
;7 (3): 557-66.
He J, Yu Y, Liu N, Zhao S. Numerical model-based relationship
between meteorological conditions and air
quality and its implication for urban air quality management.
International Journal of Environment and Pollution.
; 53 (3/4): 265.
Tso GKF, Yau KKW. Predicting electricity energy consumption:
A comparison of regression analysis, decision
tree and neural networks. Energy. 2007; 32 (9):1761-8.
Reich SL, Gomez DR, Dawidowski LE. Artificial neural
network for the identification of unknown air pollution
sources. Atmospheric Environment. 1999; 33 (18):
Birant D. Comparison of Decision Tree Algorithms for
Predicting Potential Air Pollutant Emissions with Data
Mining Models. Journal of Environmental Informatics.
Moustris KP, Zafirakis D, Alamo DH, Nebot Medina
RJ, Kaldellis JK. 24-h Ahead Wind Speed Prediction
for the Optimum Operation of Hybrid Power Stations
with the Use of Artificial Neural Networks. Perspectives
on Atmospheric Sciences. 2017: 409-14.
Jiang D, Zhang Y, Hu X, Zeng Y, Tan J, Shao D. Progress
in developing an ANN model for air pollution index
forecast. Atmospheric Environment. 2004; 38 (40):
Wu S, Feng Q, Du Y, Li X. Artificial Neural Network
Models for Daily PM10 Air Pollution Index Prediction
in the Urban Area of Wuhan, China. Environmental Engineering
Science. 2011; 28 (5): 357-63.
Vakili M, Sabbagh-Yazdi SR, Khosrojerdi S, Kalhor
K. Evaluating the effect of particulate matter pollution
on estimation of daily global solar radiation using artificial
neural network modeling based on meteorological
data. Journal of Cleaner Production. 2017;141 ( Supplement
Tong W, Hong H, Fang H, Xie Q, Perkins R. Decision
Forest: Combining the Predictions of Multiple Independent
Decision Tree Models. American Chemical Society. 2003;43 (2): 525-31.
Sousa SIV, Martins FG, Alvimferraz MCM, Pereira
MC. Multiple linear regression and artificial neural networks
based on principal components to predict ozone
concentrations. Environmental Modelling & Software.
; 22 (1): 97-103.
Rehman S, Mohandes M. Artificial neural network
estimation of global solar radiation using air temperature
and relative humidity. Energy Policy. 2008; 36 (2):
Ibarra-Berastegi G, Elias A, Barona A, Saenz J, Ezcurra
A, Diaz de Argandoña J. From diagnosis to prognosis
for forecasting air pollution using neural networks: Air
pollution monitoring in Bilbao. Environmental Modelling
& Software. 2008; 23 (5): 622-37.
Kolehmainen M, Martikainen H, Ruuskanen J. Neural
networks and periodic components used in air quality
forecasting. Atmospheric Environment. 2001;35 (5):
Gardner MW, Dorling SR. Neural network modelling
and prediction of hourly NOx and NO2 concentrations
in urban air in London. Atmospheric Environment.
; 33 (5):709-19.
Moustris KP, Ziomas IC, Paliatsos AG. 3-Day-Ahead
Forecasting of Regional Pollution Index for the Pollutants
NO2, CO, SO2, and O3 Using Artificial Neural
Networks in Athens, Greece. Water, Air, & Soil Pollution.
; 209 (1): 29-43.