PM10, SO2 hava kirleticilerinin çoklu doğrusal regresyon ve yapay sinir ağları ile sezonsal tahmini
Year 2023,
, 163 - 179, 15.08.2023
Burak Kotan
,
Arzu Erener
Abstract
Nüfus artışıyla birlikte kentleşme, sanayileşme ve taşıt sayısındaki artışlar hava kirliliğinin artmasına sebep olmaktadır. Hava kirliliği insan ve çevre sağlığına zarar vermektedir. Bu nedenle bu çalışmada hava kirliliğinin önlenmesi, tedbirlerin alınması ve planlamaların yapılabilmesi için 1 yıl önceden hava kirliliğinin tahmin edilmesi amaçlanmıştır. Hava kirliliği parametrelerinden olan PM10 ve SO2 parametrelerinin mevsimsel ortalamalarının tahmin edilmesi için çoklu doğrusal regresyon analizi ve yapay sinir ağı yöntemleri kullanılmıştır. Kocaeli, Türkiye’nin sayılı sanayi bölgelerinden olması dolayısı ile çalışma alanı olarak seçilmiştir. Tahmin modellerinde meteorolojik veriler, kirletici konsantrasyonları, kentleşme, sanayileşme, topografik ve demografik veriler kullanılmıştır. Çalışmada enterpolasyon yöntemleri ile verilerin sürekliliğinin sağlanmasında, veri setlerinin oluşturulmasında ve haritaların yapılmasında coğrafi bilgi sistemleri kullanılmıştır. Çalışmada 2008 ile 2017 yılları arasındaki veriler ile tahmin modelleri oluşturulmuştur. Oluşturulan modellere 2018 verileri sunularak 2019 yılına ait tahmin değerleri elde edilmiştir. Elde edilen tahmin değerleri 2019 yılına ait gerçek değerler ile karşılaştırılmıştır. Model performansları ortalama mutlak yüzde hata (OMYH) değerine göre değerlendirilmiştir. Çalışma sonucunda yapay sinir ağının çoklu regresyon analizine göre performansının daha yüksek olduğu tespit edilmiştir. Ayrıca, mekânsal verilerin hava kirliliği tahmin modellerinde coğrafi bilgi sistemleri ile kullanılabilirliği gösterilmiştir.
Thanks
Bu makale Burak Kotan tarafından Prof. Dr. Arzu Erener danışmanlığında Kocaeli Üniversitesi Fen Bilimleri Enstitüsü Jeodezi ve Jeoinformasyon Mühendisliği Anabilim dalında hazırlanan “Hava Kirliliği Parametrelerinin Yapay Sinir Ağı (YSA) ve Çoklu Regresyon Algoritması ile Modellenmesi: Kocaeli Örneği”
adlı yüksek lisans tezinden oluşturulmuştur.
References
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- Campos, D. S., de Souza Tadano, Y., Alves, T. A., Siqueira, H. V., & de Nóbrega Marinho, M. H. (2020). Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology, 42, e48203-e48203.
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- Çetin, M., Onac, A., Sevik, H., & Sen, B. (2019). Temporal and regional change of some air pollution parameters in Bursa, Air Quality, Atmosphere & Health, 12(3), 311-316.
- Chen, Y., Shi, R., Shu, S., & Gao, W. (2013). Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis, Atmospheric Environment, 74, 346-359.
- Çuhadar, M., & Kayacan, C. (2005). Yapay sinir ağları kullanılarak konaklama işletmelerinde doluluk oranı tahmini: Türkiye'deki konaklama işletmeleri üzerine bir deneme, Anatolia: Turizm Araştırmaları Dergisi, 16(1), 24-30.
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Sahlan S, Yunus MA, Wahap, AR. Modeling, Design and Simulation of Systems, 685-696, Singapore, Springer Nature.
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- Dutta, A., & Jinsart, W. (2021). Air pollution in Indian cities and comparison of MLR, ANN and CART models for predicting PM10 concentrations in Guwahati, India, Asian Journal of Atmospheric Environment, 15(1).
- Erener, A., Sarp, G., & Yıldırım, Ö. (2019). Seasonal air pollution investigation and relation analysis of air pollution parameters to meteorological data (Kocaeli/Turkey). Editors: El-Askary HM, Lee S, Heggy E, Pradhan B. Advances in Remote Sensing and Geo Informatics Applications, 355-358, Cham, Switzerland, Springer.
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- Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical Normalization and Back Propagation for Classification, International Journal of Computer Theory and Engineering, 3(1), 89-93.
- Jiang, L., Zhou, H., Bai, L., & Zhou, P. (2018). Does foreign direct investment drive environmental degradation in China? An empirical study based on air quality index from a spatial perspective, Journal of Cleaner Production, 176, 864-872.
- Jumaah, H. J., Ameen, M. H., Kalantar, B., & Rizeei, H. M. (2019). Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia, Geomatics, Natural Hazards and Risk, 10(1), 2185-2199.
- Kaplan, G., & Avdan, Z. Y. (2020). Space-borne air pollution observation from sentinel-5p tropomi: Relationship between pollutants, geographical and demographic data. International Journal of Engineering and Geosciences, 5(3), 130-137.
- Kolasa-Wiecek, A., & Suszanowicz, D. (2019). Air pollution in European countries and life expectancy—modelling with the use of neural network, Air Qualıty Atmosphere and Health, 12(11), 1335-1345.
- Lee, C. F., Chen, H. Y., & Lee, J. (2019). Financial Econometrics, Mathematics and Statistics, New York, Springer.
- Lewis, C. D. (1982). Industrial and Business Forecasting Methods, Londra, Butterworths Publishing.
- Li, L., Qian, J., Ou, C. Q., Zhou, Y. X., Guo, C., & Guo, Y. (2014). Spatial and temporal analysis of air pollution index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011, Environmental Pollution, 190, 75-81.
- Lin, X., & Wang, D. (2016). Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China, Journal of Geographical Sciences, 26, 1533–1549.
- Liu, H., Fang, C., Zhang, X., Wang, Z., Bao, C., & Li, F. (2017). The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach. Journal of Cleaner Production, 165, 323-333.
- Lu, D., Xu, J., Yang, D., & Zhao, J. (2017). Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014, Atmospheric Pollution Research, 8(6), 1151-1159.
- Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model, Clean Technologies and Environmental Policy, 21(6), 1341-1352.
- Mansor, A., Abdullah, S., Che Dom, N., Mohd Napi, N., Ahmed, A., Ismail, M., & Zulkifli, M. (2021). Three-hour-ahead of multiple linear regression (MLR) models for particulate matter, International Journal of Design & Nature and Ecodynamics, 16(1), 53-59.
- Moustris, K. P., Ziomas, I. C., & Paliatsos, A. G. (2010). 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece, Water, Air, & Soil Pollution, 209(1), 29-43.
- Moustris, K., Nastos, P., Larissi, I., & Paliatsos, A. (2012). Application of multiple linear regression models and artificial neural networks on the surface ozone forecast in the greater Athens Area, Greece, Advances in Meteorology, 2012.
- Özcan, H. K., Şahin, Ü., Bayat, C., & Uçan, O. N. (2006). Modeling of trophospheric ozone (O3) concentrations using cellular neural networks, Journal of the Faculty of Engineering and Architecture of Gazi University, 21(2), 239-245.
- Tsagris, M., & Pandis, N. (2021). Multicollinearity, American Journal of Orthodontics and Dentofacial Orthopedics, 159(5), 695-696.
- URL-1:https://www.harita.gov.tr/urun/il-ve-ilce-yuz-olcumleri/176 Erişim tarihi: 16 Mart 2022
- URL-2:https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2020-37210 Erişim tarihi: 16 Mart 2022
- URL-3:http://www.kocaeli.gov.tr/kocaeli-ekonomisinde-sanayinin-yeri Erişim tarihi: 16 Mart 2022
- URL-4:http://www.kocaeli.gov.tr/lojistigin-merkezinde-bir-kent-kocaeli Erişim tarihi: 16 Mart 2022
- Vorapracha, P., Phonprasert, P., Khanaruksombat, S., & Pijarn, N. (2015). A Comparison of Spatial Interpolation Methods for predicting concentrations of Particle Pollution (PM10), International Journal of Chemical, Environmental & Biological Sciences, 3(4), 302-306.
- Voss, D. S. (2005) Multicollinearity. Editors: Kempf-Leonard K. Encyclopedia of Social Measurement, 759-770, New York, Elsevier.
- Wang, P., Liu, Y., Qin, Z., & Zhang, G. (2015). A novel hybrid forecasting model for PM10 and SO2 daily concentrations, Science of the Total Environment, 505, 1202-1212.
- Witt, S. F., & Witt, C. A. (1992). Modeling and Forecasting Demand in Tourism, Londra, Academic Press.
- Yadav, V., & Nath, S. (2020a). Artificial neural networks based condition monitoring of air pollutants for Allahabad cities in India, Editors: Malik H, Iqbal A, Yadav AK. Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems, 423-437, Singapore, Springer.
- Yadav, V., & Nath, S. (2020b). Novel Application of Artificial Neural Network Techniques for Prediction of Air Pollutants Using Stochastic Variables for Health Monitoring: A Review. Editors: Malik H, Iqbal A, Yadav AK. Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems: Novel Methods for Condition Monitoring and Diagnostics, 231-245, Singapore, Springe.
- Yılmaz, A. (2017). İklim parametrelerinin hava kirliliği parametreleri üzerine etkisi: Bolu ili örneği, Journal of Current Researches on Social Sciences, 7(2), 413-436.
- Yüksek, A. G., Bircan, H., Zontul, M., & Kaynar, O. (2007). Sivas ilinde yapay sinir ağları ile hava kalitesi modelinin oluşturulması üzerine bir uygulama, Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 8(1), 97-112.
Year 2023,
, 163 - 179, 15.08.2023
Burak Kotan
,
Arzu Erener
References
- Adachi, K. (2020). Matrix-Based Introduction to Multivariate Data Analysis, Singapore, Springer.
- Akyürek, Ö., Arslan, O., & Karademir, A. (2013). SO2 ve PM10 hava kirliliği parametrelerinin CBS ile konumsal analizi: Kocaeli örneği, TMMOB Coğrafi Bilgi Sistemleri Kongresi, 11-13 Kasım 2013, Ankara, Türkiye.
- Alvarez, H., Echeverria, R., Alvarez, P., & Krupa, S. (2013). Air quality standards for particulate matter (PM) at high altitude cities, Environmental Pollution, 173, 255-256.
- Aydınoğlu, A. Ç., Bovkır, R., & Bulut, M. (2022). Akıllı şehirlerde büyük coğrafi veri yönetimi ve analizi: hava kalitesi örneği. Geomatik, 7(3), 174-186.
- Bai, L., Jiang, L., Yang, D., & Liu, Y. (2019). Quantifying the spatial heterogeneity influences of natural and socioeconomic factors and their interactions on air pollution using the geographical detector method: A case study of the Yangtze River Economic Belt, China”. Journal of Cleaner Production, 232, 692-704.
- Bannister, E., Cai, X., Zhong, J., & MacKenzie, A. (2021). Neighbourhood-scale flow regimes and pollution transport in cities, Boundary-Layer Meteorology, 179(2), 259-289.
- Campos, D. S., de Souza Tadano, Y., Alves, T. A., Siqueira, H. V., & de Nóbrega Marinho, M. H. (2020). Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology, 42, e48203-e48203.
- Caselli, M., Trizio, L., de Gennaro, G., & Ielpo, P. (2009). A Simple Feedforward Neural Network for the PM10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model, Water Air and Soil Pollution, 201(1-4), 365-377.
- Çetin, M., Onac, A., Sevik, H., & Sen, B. (2019). Temporal and regional change of some air pollution parameters in Bursa, Air Quality, Atmosphere & Health, 12(3), 311-316.
- Chen, Y., Shi, R., Shu, S., & Gao, W. (2013). Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis, Atmospheric Environment, 74, 346-359.
- Çuhadar, M., & Kayacan, C. (2005). Yapay sinir ağları kullanılarak konaklama işletmelerinde doluluk oranı tahmini: Türkiye'deki konaklama işletmeleri üzerine bir deneme, Anatolia: Turizm Araştırmaları Dergisi, 16(1), 24-30.
- Dahari, N., & Wahid, H. (2017). Determination of modeling parameters for a low cost air pollution measurement system using feed forward neural networks., Editors: Mohamed Ali MS, Wahid H, Mohd Subha NA,
Sahlan S, Yunus MA, Wahap, AR. Modeling, Design and Simulation of Systems, 685-696, Singapore, Springer Nature.
- Du, M., Liu, W., & Hao, Y. (2021). Spatial correlation of air pollution and its causes in Northeast China, International Journal of Environmental Research and Public Health, 18(21).
- Dutta, A., & Jinsart, W. (2021). Air pollution in Indian cities and comparison of MLR, ANN and CART models for predicting PM10 concentrations in Guwahati, India, Asian Journal of Atmospheric Environment, 15(1).
- Erener, A., Sarp, G., & Yıldırım, Ö. (2019). Seasonal air pollution investigation and relation analysis of air pollution parameters to meteorological data (Kocaeli/Turkey). Editors: El-Askary HM, Lee S, Heggy E, Pradhan B. Advances in Remote Sensing and Geo Informatics Applications, 355-358, Cham, Switzerland, Springer.
- Field, A. (2009). Discovering Statistics Using SPSS, Dubai, SAGE Publications.
- Forsyth, D. (2019). Applied Machine Learning. Cham, Springer.
- George, D., & Mallery, P. (2010). SPSS for Windows step by step: a simple guide and reference, Boston, Allyn & Bacon.
- Ghaemi, Z., Alimohammadi, A., & Farnaghi, M. (2018). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran, Environmental Monitoring and Assessment, 190(5).
- Ghatak, A. (2017). Machine Learning with R, Singapore, Springer.
- Gonzalez-Enrique, J., Ruiz-Aguilar, J. J., Moscoso-Lopez, J. A., Urda, D., Deka, L., & Turias, I. J. (2021). Artificial neural networks, sequence-to-sequence LSTMs, and exogenous variables as analytical tools for NO2 (air pollution) forecasting: A case study in the bay of algeciras (Spain), Sensors, 21(5), 1770.
- Haglin, J. M., Jimenez, G., & Eltorai, A. E. (2019). Artificial neural networks in medicine, Health and Technology, 9(1), 1-6.
- Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical Normalization and Back Propagation for Classification, International Journal of Computer Theory and Engineering, 3(1), 89-93.
- Jiang, L., Zhou, H., Bai, L., & Zhou, P. (2018). Does foreign direct investment drive environmental degradation in China? An empirical study based on air quality index from a spatial perspective, Journal of Cleaner Production, 176, 864-872.
- Jumaah, H. J., Ameen, M. H., Kalantar, B., & Rizeei, H. M. (2019). Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia, Geomatics, Natural Hazards and Risk, 10(1), 2185-2199.
- Kaplan, G., & Avdan, Z. Y. (2020). Space-borne air pollution observation from sentinel-5p tropomi: Relationship between pollutants, geographical and demographic data. International Journal of Engineering and Geosciences, 5(3), 130-137.
- Kolasa-Wiecek, A., & Suszanowicz, D. (2019). Air pollution in European countries and life expectancy—modelling with the use of neural network, Air Qualıty Atmosphere and Health, 12(11), 1335-1345.
- Lee, C. F., Chen, H. Y., & Lee, J. (2019). Financial Econometrics, Mathematics and Statistics, New York, Springer.
- Lewis, C. D. (1982). Industrial and Business Forecasting Methods, Londra, Butterworths Publishing.
- Li, L., Qian, J., Ou, C. Q., Zhou, Y. X., Guo, C., & Guo, Y. (2014). Spatial and temporal analysis of air pollution index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011, Environmental Pollution, 190, 75-81.
- Lin, X., & Wang, D. (2016). Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China, Journal of Geographical Sciences, 26, 1533–1549.
- Liu, H., Fang, C., Zhang, X., Wang, Z., Bao, C., & Li, F. (2017). The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach. Journal of Cleaner Production, 165, 323-333.
- Lu, D., Xu, J., Yang, D., & Zhao, J. (2017). Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014, Atmospheric Pollution Research, 8(6), 1151-1159.
- Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model, Clean Technologies and Environmental Policy, 21(6), 1341-1352.
- Mansor, A., Abdullah, S., Che Dom, N., Mohd Napi, N., Ahmed, A., Ismail, M., & Zulkifli, M. (2021). Three-hour-ahead of multiple linear regression (MLR) models for particulate matter, International Journal of Design & Nature and Ecodynamics, 16(1), 53-59.
- Moustris, K. P., Ziomas, I. C., & Paliatsos, A. G. (2010). 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece, Water, Air, & Soil Pollution, 209(1), 29-43.
- Moustris, K., Nastos, P., Larissi, I., & Paliatsos, A. (2012). Application of multiple linear regression models and artificial neural networks on the surface ozone forecast in the greater Athens Area, Greece, Advances in Meteorology, 2012.
- Özcan, H. K., Şahin, Ü., Bayat, C., & Uçan, O. N. (2006). Modeling of trophospheric ozone (O3) concentrations using cellular neural networks, Journal of the Faculty of Engineering and Architecture of Gazi University, 21(2), 239-245.
- Tsagris, M., & Pandis, N. (2021). Multicollinearity, American Journal of Orthodontics and Dentofacial Orthopedics, 159(5), 695-696.
- URL-1:https://www.harita.gov.tr/urun/il-ve-ilce-yuz-olcumleri/176 Erişim tarihi: 16 Mart 2022
- URL-2:https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2020-37210 Erişim tarihi: 16 Mart 2022
- URL-3:http://www.kocaeli.gov.tr/kocaeli-ekonomisinde-sanayinin-yeri Erişim tarihi: 16 Mart 2022
- URL-4:http://www.kocaeli.gov.tr/lojistigin-merkezinde-bir-kent-kocaeli Erişim tarihi: 16 Mart 2022
- Vorapracha, P., Phonprasert, P., Khanaruksombat, S., & Pijarn, N. (2015). A Comparison of Spatial Interpolation Methods for predicting concentrations of Particle Pollution (PM10), International Journal of Chemical, Environmental & Biological Sciences, 3(4), 302-306.
- Voss, D. S. (2005) Multicollinearity. Editors: Kempf-Leonard K. Encyclopedia of Social Measurement, 759-770, New York, Elsevier.
- Wang, P., Liu, Y., Qin, Z., & Zhang, G. (2015). A novel hybrid forecasting model for PM10 and SO2 daily concentrations, Science of the Total Environment, 505, 1202-1212.
- Witt, S. F., & Witt, C. A. (1992). Modeling and Forecasting Demand in Tourism, Londra, Academic Press.
- Yadav, V., & Nath, S. (2020a). Artificial neural networks based condition monitoring of air pollutants for Allahabad cities in India, Editors: Malik H, Iqbal A, Yadav AK. Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems, 423-437, Singapore, Springer.
- Yadav, V., & Nath, S. (2020b). Novel Application of Artificial Neural Network Techniques for Prediction of Air Pollutants Using Stochastic Variables for Health Monitoring: A Review. Editors: Malik H, Iqbal A, Yadav AK. Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems: Novel Methods for Condition Monitoring and Diagnostics, 231-245, Singapore, Springe.
- Yılmaz, A. (2017). İklim parametrelerinin hava kirliliği parametreleri üzerine etkisi: Bolu ili örneği, Journal of Current Researches on Social Sciences, 7(2), 413-436.
- Yüksek, A. G., Bircan, H., Zontul, M., & Kaynar, O. (2007). Sivas ilinde yapay sinir ağları ile hava kalitesi modelinin oluşturulması üzerine bir uygulama, Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 8(1), 97-112.