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Air Quality Assessment and Forecasting Using Neural Network Model
 
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1
Department of Mechanical Engineering, The University of Jordan, Amman 11942, Jordan
2
Department of Mechanical and Industrial Engineering, American University of Ras Al Khaimah, 10021 United Arab Emirates
CORRESPONDING AUTHOR
Ahmad Sakhrieh   

Department of Mechanical Engineering, The University of Jordan, Amman 11942, Jordan
Publication date: 2021-06-06
 
J. Ecol. Eng. 2021; 22(6):1–11
 
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ABSTRACT
Air pollution is a major obstacle faced by all countries and impact the environment, public health, socioeconomics, and, agriculture. In this study, air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast daily average pollutants’ levels in Amman, Jordan. The model was built using MATLAB software. The model utilized a Marquardt–Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Air pollutants historical measurements were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. Meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations