Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network
Suspended 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 pre...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2016-01-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_560_f1de11875bc661841af152fd207b422b.pdf |
| _version_ | 1818154811169701888 |
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| author | M. Asghari H. Nematzadeh |
| author_facet | M. Asghari H. Nematzadeh |
| author_sort | M. Asghari |
| collection | DOAJ |
| description | Suspended 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 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. |
| first_indexed | 2024-12-11T14:32:26Z |
| format | Article |
| id | doaj.art-22fe74b9d29645b99b53a33cc77692b4 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-11T14:32:26Z |
| publishDate | 2016-01-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-22fe74b9d29645b99b53a33cc77692b42022-12-22T01:02:21ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442016-01-0141495410.5829/idosi.JAIDM.2016.04.01.06560Predicting air pollution in Tehran: Genetic algorithm and back propagation neural networkM. Asghari0H. Nematzadeh1Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, IranDepartment of Computer Engineering, Sari Branch, Islamic Azad University, Sari, IranSuspended 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 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.http://jad.shahroodut.ac.ir/article_560_f1de11875bc661841af152fd207b422b.pdfArtificial Neural NetworksGenetic algorithmair pollutionPM10 |
| spellingShingle | M. Asghari H. Nematzadeh Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network Journal of Artificial Intelligence and Data Mining Artificial Neural Networks Genetic algorithm air pollution PM10 |
| title | Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network |
| title_full | Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network |
| title_fullStr | Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network |
| title_full_unstemmed | Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network |
| title_short | Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network |
| title_sort | predicting air pollution in tehran genetic algorithm and back propagation neural network |
| topic | Artificial Neural Networks Genetic algorithm air pollution PM10 |
| url | http://jad.shahroodut.ac.ir/article_560_f1de11875bc661841af152fd207b422b.pdf |
| work_keys_str_mv | AT masghari predictingairpollutionintehrangeneticalgorithmandbackpropagationneuralnetwork AT hnematzadeh predictingairpollutionintehrangeneticalgorithmandbackpropagationneuralnetwork |