Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 7 Sayı: 1, 17 - 24, 15.01.2023
https://doi.org/10.31127/tuje.1005514

Öz

Kaynakça

  • Dagsuyu, C. (2020). Process capability and risk assessment for air quality: An integrated approach. Human and Ecological Risk Assessment: An International Journal, 26(2), 394–405.
  • Vineis, P., & Husgafvel-Pursiainen, K. (2005). Air pollution and cancer: Biomarker studies in human populations †. Carcinogenesis, 26(11), 1846–1855.
  • Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A., Peters, A., Siscovick, D., Smith, S. C., Whitsel, L., & Kaufman, J. D. (2010). Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement from the American Heart Association. Circulation, 121(21), 2331–2378.
  • Kelly, F. J., & Fussell, J. C. (2011). Air pollution and airway disease: Air pollution and airway disease. Clinical & Experimental Allergy, 41(8), 1059–1071.
  • Gold, D. R., & Samet, J. M. (2013). Air pollution, climate, and heart disease. Circulation, 128(21).
  • Łatka, P., D. Nowakowska, K. Nowomiejska, and R. Rejdak. 2018. How air pollution affects the eyes—A review. Ophthalmology Journal 3 (2):58–62.
  • Ghorani-Azam, A., Riahi-Zanjani, B., & Balali-Mood, M. (2016). Effects of air pollution on human health and practical measures for prevention in Iran. Journal of Research in Medical Sciences, 21(1), 65.
  • Flemming, J., Stern, R., & Yamartino, R. (2005). A new air quality regime classification scheme for O, NO, SO and PM10 observations sites. Atmospheric Environment, 39(33), 6121–6129.
  • https://sim.csb.gov.tr/
  • Kaur, P., Sharma, M., & Mittal, M. (2018). Big Data and Machine Learning Based Secure Healthcare Framework. Procedia Computer Science, 132, 1049–1059.
  • Philibert, A., Loyce, C., & Makowski, D. (2013). Prediction of N2O emission from local information with Random Forest. Environmental Pollution, 177, 156–163.
  • Kleine Deters, J., Zalakeviciute, R., Gonzalez, M., & Rybarczyk, Y. (2017). Modeling PM 2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters. Journal of Electrical and Computer Engineering, 2017, 1–14.
  • Deleawe, S., Kusznir, J., Lamb, B., & Cook, D. J. (2010). Predicting air quality in smart environments. Journal of Ambient Intelligence and Smart Environments, 2(2), 145–154.
  • Ip, W. F., Vong, C. M., Yang, J. Y., & Wong, P. K. (2010). Least Squares Support Vector Prediction for Daily Atmospheric Pollutant Level. 2010 IEEE/ACIS 9th International Conference on Computer and Information Science, 23–28.
  • Yu, R., Yang, Y., Yang, L., Han, G., & Move, O. (2016). RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems. Sensors, 16(1), 86.
  • Sethi, J. K., & Mittal, M. (2019). A new feature selection method based on machine learning technique for air quality dataset. Journal of Statistics and Management Systems, 22(4), 697–705.
  • Li, H., Wang, J., Li, R., & Lu, H. (2019). Novel analysis–forecast system based on multi-objective optimization for air quality index. Journal of Cleaner Production, 208, 1365–1383.
  • Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. IJ Network Security, 18(3), 420-432.
  • Peng, H., Ying, C., Tan, S., Hu, B., & Sun, Z. (2018). An Improved Feature Selection Algorithm Based on Ant Colony Optimization. IEEE Access, 6, 69203–69209.
  • Ghosh, M., Guha, R., Sarkar, R., & Abraham, A. (2020). A wrapper-filter feature selection technique based on ant colony optimization. Neural Computing and Applications, 32(12), 7839–7857.
  • Jeyasingh, S., & Veluchamy, M. (2017). Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. Asian Pacific Journal of Cancer Prevention, 18(5).
  • Qasim, O. S., & Algamal, Z. Y. (2020). Feature Selection Using Different Transfer Functions for Binary Bat Algorithm. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 697–706.
  • Pandey, A. C., Rajpoot, D. S., & Saraswat, M. (2020). Feature selection method based on hybrid data transformation and binary binomial cuckoo search. Journal of Ambient Intelligence and Humanized Computing, 11(2), 719–738.
  • Gunavathi, C., & Premalatha, K. (2015). Cuckoo search optimisation for feature selection in cancer classification: A new approach. International Journal of Data Mining and Bioinformatics, 13(3), 248.
  • Pan, F., Ye, C., Wang, K., & Cao, J. (2013). Research on the Vehicle Routing Problem with Time Windows Using Firefly Algorithm. Journal of Computers, 8(9), 2256–2261.
  • Alweshah, M. (2014). Firefly Algorithm with Artificial Neural Network for Time Series Problems. Research Journal of Applied Sciences, Engineering and Technology, 7(19), 3978–3982.
  • Abdelaziz, A. Y., Mekhamer, S. F., Badr, M., Algabalawy, M.A. (2015). The firefly meta-heuristic algorithms: developments and applications. International Electrical Engineering Journal (IEEJ), 6(7),1945–1952
  • Kumar, A., & Khorwal, R. (2017). Firefly Algorithm for Feature Selection in Sentiment Analysis. In H. S. Behera & D. P. Mohapatra (Eds.), Computational Intelligence in Data Mining (Vol. 556, pp. 693–703). Springer Singapore.
  • Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., & Li, Y. (2018). A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences, 438, 95–106.
  • Sawhney, R., Mathur, P., & Shankar, R. (2018). A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis. In O. Gervasi, B. Murgante, S. Misra, E. Stankova, C. M. Torre, A. M. A. C. Rocha, D. Taniar, B. O. Apduhan, E. Tarantino, & Y. Ryu (Eds.), Computational Science and Its Applications – ICCSA 2018 (Vol. 10960, pp. 438–449). Springer International Publishing.
  • Dash, S., Thulasiram, R., & Thulasiraman, P. (2019). Modified firefly algorithm with chaos theory for feature selection: A predictive model for medical data. International Journal of Swarm Intelligence Research (IJSIR), 10(2), 1-20.
  • Kira, K., & Rendell, L. A. (1992). A Practical Approach to Feature Selection. In Machine Learning Proceedings 1992 (pp. 249–256). Elsevier.
  • Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In F. Bergadano & L. Raedt (Eds.), Machine Learning: ECML-94 (Vol. 784, pp. 171–182). Springer Berlin Heidelberg.
  • http://www.cs.waikato.ac.nz/ml/weka
  • Robnik-Šikonja, M., & Kononenko, I. (2003). [No title found]. Machine Learning, 53(1/2), 23–69.
  • Yang, X.-S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press.
  • Bäck, T. (1996). Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press.
  • Ho, T.K. (1995) Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995, 278-282.
  • https://sim.csb.gov.tr/Services/AirQuality
  • Gao, F. (2013). Evaluation of the Chinese new air quality index (GB3095-2012): based on comparison with the US AQI system and the WHO AQGs.
  • Han, J. and Kamber, M. (2006) Data Mining: Concepts and Techniques. 2nd Edition, Morgan Kaufmann Publishers, San Francisco.

Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey

Yıl 2023, Cilt: 7 Sayı: 1, 17 - 24, 15.01.2023
https://doi.org/10.31127/tuje.1005514

Öz

Air pollution in cities is a serious environmental issue. In Turkey, the air quality index values of the measurement stations are calculated according to European Union standards. There are many kinds of measurement parameters (features) and 6 different kinds of air quality classes according to measurement stations in Turkey. Non-valuable features can be eliminated effectively with feature selection methods without any performance loss in classification. This study aims to investigate, analyze and implement a feature selection method using the FireFly Optimization Algorithm (FOA) approach. In the study, data from measurement stations for the Zonguldak region, which is known as the most polluted region in Turkey, are obtained and analyzed. Along with the acquired data, new features have been added such as day type day slots and the Covid19 feature since it is thought that curfew restrictions have an impact on air quality. The results were compared with a filter-based feature selection algorithm namely ReliefF. Experimental results show that FOA based feature selection method outperforms the ReliefF method at classification using the Random Forest classifier for air pollution even if with a fewer number of features. The Macro averaged F-score of the data set is increased from 0.685 to 0.988 using the FOA-based feature selection method.  

Kaynakça

  • Dagsuyu, C. (2020). Process capability and risk assessment for air quality: An integrated approach. Human and Ecological Risk Assessment: An International Journal, 26(2), 394–405.
  • Vineis, P., & Husgafvel-Pursiainen, K. (2005). Air pollution and cancer: Biomarker studies in human populations †. Carcinogenesis, 26(11), 1846–1855.
  • Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A., Peters, A., Siscovick, D., Smith, S. C., Whitsel, L., & Kaufman, J. D. (2010). Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement from the American Heart Association. Circulation, 121(21), 2331–2378.
  • Kelly, F. J., & Fussell, J. C. (2011). Air pollution and airway disease: Air pollution and airway disease. Clinical & Experimental Allergy, 41(8), 1059–1071.
  • Gold, D. R., & Samet, J. M. (2013). Air pollution, climate, and heart disease. Circulation, 128(21).
  • Łatka, P., D. Nowakowska, K. Nowomiejska, and R. Rejdak. 2018. How air pollution affects the eyes—A review. Ophthalmology Journal 3 (2):58–62.
  • Ghorani-Azam, A., Riahi-Zanjani, B., & Balali-Mood, M. (2016). Effects of air pollution on human health and practical measures for prevention in Iran. Journal of Research in Medical Sciences, 21(1), 65.
  • Flemming, J., Stern, R., & Yamartino, R. (2005). A new air quality regime classification scheme for O, NO, SO and PM10 observations sites. Atmospheric Environment, 39(33), 6121–6129.
  • https://sim.csb.gov.tr/
  • Kaur, P., Sharma, M., & Mittal, M. (2018). Big Data and Machine Learning Based Secure Healthcare Framework. Procedia Computer Science, 132, 1049–1059.
  • Philibert, A., Loyce, C., & Makowski, D. (2013). Prediction of N2O emission from local information with Random Forest. Environmental Pollution, 177, 156–163.
  • Kleine Deters, J., Zalakeviciute, R., Gonzalez, M., & Rybarczyk, Y. (2017). Modeling PM 2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters. Journal of Electrical and Computer Engineering, 2017, 1–14.
  • Deleawe, S., Kusznir, J., Lamb, B., & Cook, D. J. (2010). Predicting air quality in smart environments. Journal of Ambient Intelligence and Smart Environments, 2(2), 145–154.
  • Ip, W. F., Vong, C. M., Yang, J. Y., & Wong, P. K. (2010). Least Squares Support Vector Prediction for Daily Atmospheric Pollutant Level. 2010 IEEE/ACIS 9th International Conference on Computer and Information Science, 23–28.
  • Yu, R., Yang, Y., Yang, L., Han, G., & Move, O. (2016). RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems. Sensors, 16(1), 86.
  • Sethi, J. K., & Mittal, M. (2019). A new feature selection method based on machine learning technique for air quality dataset. Journal of Statistics and Management Systems, 22(4), 697–705.
  • Li, H., Wang, J., Li, R., & Lu, H. (2019). Novel analysis–forecast system based on multi-objective optimization for air quality index. Journal of Cleaner Production, 208, 1365–1383.
  • Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. IJ Network Security, 18(3), 420-432.
  • Peng, H., Ying, C., Tan, S., Hu, B., & Sun, Z. (2018). An Improved Feature Selection Algorithm Based on Ant Colony Optimization. IEEE Access, 6, 69203–69209.
  • Ghosh, M., Guha, R., Sarkar, R., & Abraham, A. (2020). A wrapper-filter feature selection technique based on ant colony optimization. Neural Computing and Applications, 32(12), 7839–7857.
  • Jeyasingh, S., & Veluchamy, M. (2017). Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. Asian Pacific Journal of Cancer Prevention, 18(5).
  • Qasim, O. S., & Algamal, Z. Y. (2020). Feature Selection Using Different Transfer Functions for Binary Bat Algorithm. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 697–706.
  • Pandey, A. C., Rajpoot, D. S., & Saraswat, M. (2020). Feature selection method based on hybrid data transformation and binary binomial cuckoo search. Journal of Ambient Intelligence and Humanized Computing, 11(2), 719–738.
  • Gunavathi, C., & Premalatha, K. (2015). Cuckoo search optimisation for feature selection in cancer classification: A new approach. International Journal of Data Mining and Bioinformatics, 13(3), 248.
  • Pan, F., Ye, C., Wang, K., & Cao, J. (2013). Research on the Vehicle Routing Problem with Time Windows Using Firefly Algorithm. Journal of Computers, 8(9), 2256–2261.
  • Alweshah, M. (2014). Firefly Algorithm with Artificial Neural Network for Time Series Problems. Research Journal of Applied Sciences, Engineering and Technology, 7(19), 3978–3982.
  • Abdelaziz, A. Y., Mekhamer, S. F., Badr, M., Algabalawy, M.A. (2015). The firefly meta-heuristic algorithms: developments and applications. International Electrical Engineering Journal (IEEJ), 6(7),1945–1952
  • Kumar, A., & Khorwal, R. (2017). Firefly Algorithm for Feature Selection in Sentiment Analysis. In H. S. Behera & D. P. Mohapatra (Eds.), Computational Intelligence in Data Mining (Vol. 556, pp. 693–703). Springer Singapore.
  • Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., & Li, Y. (2018). A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences, 438, 95–106.
  • Sawhney, R., Mathur, P., & Shankar, R. (2018). A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis. In O. Gervasi, B. Murgante, S. Misra, E. Stankova, C. M. Torre, A. M. A. C. Rocha, D. Taniar, B. O. Apduhan, E. Tarantino, & Y. Ryu (Eds.), Computational Science and Its Applications – ICCSA 2018 (Vol. 10960, pp. 438–449). Springer International Publishing.
  • Dash, S., Thulasiram, R., & Thulasiraman, P. (2019). Modified firefly algorithm with chaos theory for feature selection: A predictive model for medical data. International Journal of Swarm Intelligence Research (IJSIR), 10(2), 1-20.
  • Kira, K., & Rendell, L. A. (1992). A Practical Approach to Feature Selection. In Machine Learning Proceedings 1992 (pp. 249–256). Elsevier.
  • Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In F. Bergadano & L. Raedt (Eds.), Machine Learning: ECML-94 (Vol. 784, pp. 171–182). Springer Berlin Heidelberg.
  • http://www.cs.waikato.ac.nz/ml/weka
  • Robnik-Šikonja, M., & Kononenko, I. (2003). [No title found]. Machine Learning, 53(1/2), 23–69.
  • Yang, X.-S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press.
  • Bäck, T. (1996). Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press.
  • Ho, T.K. (1995) Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995, 278-282.
  • https://sim.csb.gov.tr/Services/AirQuality
  • Gao, F. (2013). Evaluation of the Chinese new air quality index (GB3095-2012): based on comparison with the US AQI system and the WHO AQGs.
  • Han, J. and Kamber, M. (2006) Data Mining: Concepts and Techniques. 2nd Edition, Morgan Kaufmann Publishers, San Francisco.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Articles
Yazarlar

Esra Saraç Eşsiz 0000-0002-2503-0084

Vahide Nida Kılıç Bu kişi benim 0000-0003-2181-9309

Murat Oturakçı 0000-0001-5946-3964

Yayımlanma Tarihi 15 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 1

Kaynak Göster

APA Saraç Eşsiz, E., Kılıç, V. N., & Oturakçı, M. (2023). Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. Turkish Journal of Engineering, 7(1), 17-24. https://doi.org/10.31127/tuje.1005514
AMA Saraç Eşsiz E, Kılıç VN, Oturakçı M. Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. TUJE. Ocak 2023;7(1):17-24. doi:10.31127/tuje.1005514
Chicago Saraç Eşsiz, Esra, Vahide Nida Kılıç, ve Murat Oturakçı. “Firefly-Based Feature Selection Algorithm Method for Air Pollution Analysis for Zonguldak Region in Turkey”. Turkish Journal of Engineering 7, sy. 1 (Ocak 2023): 17-24. https://doi.org/10.31127/tuje.1005514.
EndNote Saraç Eşsiz E, Kılıç VN, Oturakçı M (01 Ocak 2023) Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. Turkish Journal of Engineering 7 1 17–24.
IEEE E. Saraç Eşsiz, V. N. Kılıç, ve M. Oturakçı, “Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey”, TUJE, c. 7, sy. 1, ss. 17–24, 2023, doi: 10.31127/tuje.1005514.
ISNAD Saraç Eşsiz, Esra vd. “Firefly-Based Feature Selection Algorithm Method for Air Pollution Analysis for Zonguldak Region in Turkey”. Turkish Journal of Engineering 7/1 (Ocak 2023), 17-24. https://doi.org/10.31127/tuje.1005514.
JAMA Saraç Eşsiz E, Kılıç VN, Oturakçı M. Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. TUJE. 2023;7:17–24.
MLA Saraç Eşsiz, Esra vd. “Firefly-Based Feature Selection Algorithm Method for Air Pollution Analysis for Zonguldak Region in Turkey”. Turkish Journal of Engineering, c. 7, sy. 1, 2023, ss. 17-24, doi:10.31127/tuje.1005514.
Vancouver Saraç Eşsiz E, Kılıç VN, Oturakçı M. Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey. TUJE. 2023;7(1):17-24.
Flag Counter