Air Quality Assessment and Forecasting Using Neural Network Model

• Autorzy: Hamdan Mohammad A. , Ata Mohammad F. Bani , Sakhrieh Ahmad H.

Identyfikatory

DOI

10.12911/22998993/137444

• Języki publikacji: EN

Abstrakty

EN

Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the 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. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The 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.

Słowa kluczowe

EN

air pollutant; ANN; MATLAB; forecasting

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2021
• Tom: Vol. 22, nr 6
• Strony: 1--11
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Hamdan Mohammad A.

• Department of Mechanical Engineering, The University of Jordan, Amman 11942, Jordan

autor

Ata Mohammad F. Bani

• Department of Mechanical Engineering, The University of Jordan, Amman 11942, Jordan

autor

Sakhrieh Ahmad H.

• Department of Mechanical Engineering, The University of Jordan, Amman 11942, Jordan
• Department of Mechanical and Industrial Engineering, American University of Ras Al Khaimah, 10021, United Arab Emirates

• https://orcid.org/0000-0001-8182-0462

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