PREDICTING AIR POLLUTION IN TEHRAN: GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK

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ASGHARI ESFANDANI M.; NEMATZADEH H.

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Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2016 | Volume:4 | Issue:1 Page(s): 49-54

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Information Journal Paper

Title

PREDICTING AIR POLLUTION IN TEHRAN: GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK

Author(s)

ASGHARI ESFANDANI M. | NEMATZADEH H. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKSQ2

GENETIC ALGORITHMQ2

AIR POLLUTIONQ3

PM10Q2

Abstract

uspended particles have deleterious effects on human health and one of the reasons why Tehran is effected is its geographically location of AIR POLLUTION. One of the most important ways to reduce AIR POLLUTION is to predict the concentration of pollutants. This paper proposed a hybrid method to predict the AIR POLLUTION in Tehran based on particulate matter less than 10 microns (PM10), and the information and data of Aghdasiyeh Weather Quality Control Station and Mehrabad Weather Station from 2007 to 2013. Generally, 11 inputs have been inserted to the model, to predict the daily concentration of PM10. For this purpose, Artificial Neural Network with Back Propagation (BP) with a middle layer and sigmoid activation function and its hybrid with GENETIC ALGORITHM (BP-GA) were used and ultimately the performance of the proposed method was compared with basic ARTIFICIAL NEURAL NETWORKS along with (BP) Based on the criteria of - R2-, RMSE and MAE. The finding shows that BP-GA R2 = 0.54889 has higher accuracy and performance. In addition, it was also found that the results are more accurate for shorter time periods and this is because the large fluctuation of data in long-term returns negative effect on network performance. Also, unregistered data have negative effect on predictions. Microsoft Excel and Matlab 2013 conducted the simulations.

Cites

Passive Sampling of Aromatic Hydrocarbons Toluene, Xylene and Ethylbenzene Using Activated Carbon Fabric in Ambient Air of Tehran

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APA: Copy

ASGHARI ESFANDANI, M., & NEMATZADEH, H.. (2016). PREDICTING AIR POLLUTION IN TEHRAN: GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 4(1), 49-54. SID. https://sid.ir/paper/255388/en

Vancouver: Copy

ASGHARI ESFANDANI M., NEMATZADEH H.. PREDICTING AIR POLLUTION IN TEHRAN: GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2016;4(1):49-54. Available from: https://sid.ir/paper/255388/en

IEEE: Copy

M. ASGHARI ESFANDANI, and H. NEMATZADEH, “PREDICTING AIR POLLUTION IN TEHRAN: GENETIC ALGORITHM AND BACK PROPAGATION NEURAL NETWORK,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 4, no. 1, pp. 49–54, 2016, [Online]. Available: https://sid.ir/paper/255388/en

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