Research Article
BibTex RIS Cite

DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ

Year 2020, Volume: 25 Issue: 3, 1547 - 1556, 31.12.2020
https://doi.org/10.17482/uumfd.809938

Abstract

Son yıllarda artan insan nüfusu ile fosil yakıt kullanımı yaygınlaşmıştır. Enerji üretimi, ulaşım, ısınma gibi birçok kullanım alanına sahip fosil yakıtların yanması sonucunda atmosfere salınan zararlı maddelerin yoğunluğu hem kentsel hem de kırsal bölgelerde insan sağlığını tehdit edecek seviyelere ulaşabilmektedir. Lokal hava kalitesini muhafaza edecek önlemler almak ve kirleticilerin zararlarını en aza indirebilmek için ileriye yönelik emisyon tahminlerinde bulunmak büyük önem arz etmektedir. Çalışmamızda yanma sonucunda açığa çıkan önemli kirleticilerden PM10 ve SO2 maddelerinin mevcut günlük kayıtları kullanarak gelecekte olması muhtemel değerleri tahmin edilmeye çalışılmıştır. Erzincan ilinde 2016-2018 yılları arasında ölçülmüş toplam 651 adet veri kullanılarak bir model oluşturulmuştur. Model oluşturma aşamasında verilerin ilk 400 adeti eğitim, geriye kalan 251 adet veri doğrulama olmak üzere ikiye ayrılmıştır. Modeller K-En Yakın Komşuluk (KNN) algoritması kullanılarak kurulmuş ve modelleme başarısını arttırmak adına önişlem süreçlerinden biri olan dalgacık dönüşüm tekniği uygulanmıştır. Dalgacık dönüşümü ile oluşturulan modellerin, tahmin başarısını büyük derecede iyileştirdiği gözlemlenmiştir. Bu çalışma uygulaması basit makine öğrenmesi yöntemlerinden olan KNN’nin hava kirliliği tahmin modellerinde kullanılabileceğini kanıtlamıştır.

References

  • Aalto, P., Hämeri, K., Paatero, P., Kulmala, M., Bellander, T., Berglind, N., ... ve Marconi, A. (2005) Aerosol particle number concentration measurements in five European cities using TSI-3022 condensation particle counter over a three-year period during health effects of air pollution on susceptible subpopulations. Journal of the Air & Waste Management Association, 55(8), 1064-1076.
  • Altunkaynak, A. ve Kartal, E. (2019) Performance comparison of continuous wavelet-fuzzy and discrete wavelet-fuzzy models for water level predictions at northern and southern boundary of Bosphorus, Ocean Engineering, 186, 106097.
  • Chaloulakou, A., Saisana, M. ve Spyrellis, N. (2003) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens, Science of the Total Environment, 313(1-3), 1-13.
  • Chen, T. M., Kuschner, W. G., Gokhale, J. Ve Shofer, S. (2007) Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects, The American journal of the medical sciences, 333(4), 249-256.
  • Dominici, F., Peng, R. D., Bell, M. L., Pham, L., McDermott, A., Zeger, S. L., & Samet, J. M. (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. Jama, 295(10), 1127-1134.
  • Gamble, J. F. ve Lewis, R. J. (1996). Health and respirable particulate (PM10) air pollution: a causal or statistical association?, Environmental health perspectives, 104(8), 838-850.
  • Gardner, M. W. ve Dorling, S. R. (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London, Atmospheric Environment, 33(5), 709-719.
  • Isakov, V., Touma, J. S. ve Khlystov, A. (2007) A method of assessing air toxics concentrations in urban areas using mobile platform measurements, Journal of the Air & Waste Management Association, 57(11), 1286-1295.
  • Kalapanidas, E. ve Avouris, N. (2001) Short-term air quality prediction using a case-based classifier Environmental Modelling & Software, 16(3), 263-272.
  • Kermani, E. F., Barani, G. A. ve Hessaroeyeh, M. G. (2018) Cavitation damage prediction on dam spillways using Fuzzy-KNN modeling. J Appl Fluid Mech, 11(2), 323-329.
  • Laden, F., Neas, L. M., Dockery, D. W., ve Schwartz, J. (2000) Association of fine particulate matter from different sources with daily mortality in six US cities, Environmental health perspectives, 108(10), 941-947.
  • Lary, D. J., Lary, T. ve Sattler, B. (2015) Using machine learning to estimate global PM2. 5 for environmental health studies, Environmental health insights, 9, EHI-S15664.
  • Nash, J. E. ve Sutcliffe, J. V. (1970) River flow forecasting through conceptual models part I—A discussion of principles, Journal of hydrology, 10(3), 282-290.
  • Ni, J., Qiao, F., Li, L. ve Di Wu, Q. (2012, July). A memetic PSO based KNN regression method for cycle time prediction in a wafer fab. In Proceedings of the 10th World Congress on Intelligent Control and Automation (pp. 474-478). IEEE.
  • Özcan, H. K., Şahin, Ü., Bayat, C. ve Uçan, O. N. (2006) İstanbul İli Tropsoferik Ozon (O3) Konsantrasyonlarının Hücresel Yapay Sinir Ağ Yöntemiyle Modellenmesi, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 21(2).
  • Yıldırım, Y. ve Bayramoğlu, M. (2006) Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak, Chemosphere, 63(9), 1575-1582.
  • Yüksek, A. G., Bircan, H., Zontul, M. ve Kaynar, O. (2007) Sivas İlinde Yapay Sinir Ağları İle Hava Kalitesi Modelinin Oluşturulması Üzerine Bir Uygulama.
  • Zhang, J. ve Ding, W. (2017) Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong, International journal of environmental research and public health, 14(2), 114.
  • Wu, J.D ve Liu, C.H. (2008) Investigation of engine faultdiagnosis using discrate wavelet transform and neural network. Expert Systems with Applications, 35(3), 1200-1213. doi:10.1016/j.eswa.2007.08.021

AIR POLUTION PREDICTION WITH WAVELET K-NEAREST NEIGHBOUR METHOD

Year 2020, Volume: 25 Issue: 3, 1547 - 1556, 31.12.2020
https://doi.org/10.17482/uumfd.809938

Abstract

In the last decades, the use of fossil fuels has become widespread with the increasing human population. The concentration of harmful substances released into the atmosphere as a result of the burning of fossil fuels, which have widely used for energy production, transportation, and heating. The burning of fossil fuels can reach levels that threaten human health in both urban and rural areas. It has great importance to estimate emission to take measures to protect local air quality and to minimize the damage of pollutants. The current study aims to predict the future concentration values of PM10 and SO2, which are important pollutants, by using available daily records. A predictive model is implemented for Erzincan city by using a total 651 data points observed for period from 2016 through 2018. In the modeling process, data are divided into two groups; 400 the data points are utilized for training and the remaining 251 data points are used for verification. The wavelet transform technique is combined with the K-Nearest Neighbor (KNN) method to develop a predictive model called as Wavelet- KNN approach for increasing the modeling success. In the present study, the wavelet-KNN approach is provided better prediction results compared to stand-alone KNN method. It is noted that the combination of wavelet with KNN tool is enhanced the prediction performance of model. This study shows that the KNN method is one of the simplest machine learning methods and can be used for prediction of air pollution models.

References

  • Aalto, P., Hämeri, K., Paatero, P., Kulmala, M., Bellander, T., Berglind, N., ... ve Marconi, A. (2005) Aerosol particle number concentration measurements in five European cities using TSI-3022 condensation particle counter over a three-year period during health effects of air pollution on susceptible subpopulations. Journal of the Air & Waste Management Association, 55(8), 1064-1076.
  • Altunkaynak, A. ve Kartal, E. (2019) Performance comparison of continuous wavelet-fuzzy and discrete wavelet-fuzzy models for water level predictions at northern and southern boundary of Bosphorus, Ocean Engineering, 186, 106097.
  • Chaloulakou, A., Saisana, M. ve Spyrellis, N. (2003) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens, Science of the Total Environment, 313(1-3), 1-13.
  • Chen, T. M., Kuschner, W. G., Gokhale, J. Ve Shofer, S. (2007) Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects, The American journal of the medical sciences, 333(4), 249-256.
  • Dominici, F., Peng, R. D., Bell, M. L., Pham, L., McDermott, A., Zeger, S. L., & Samet, J. M. (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. Jama, 295(10), 1127-1134.
  • Gamble, J. F. ve Lewis, R. J. (1996). Health and respirable particulate (PM10) air pollution: a causal or statistical association?, Environmental health perspectives, 104(8), 838-850.
  • Gardner, M. W. ve Dorling, S. R. (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London, Atmospheric Environment, 33(5), 709-719.
  • Isakov, V., Touma, J. S. ve Khlystov, A. (2007) A method of assessing air toxics concentrations in urban areas using mobile platform measurements, Journal of the Air & Waste Management Association, 57(11), 1286-1295.
  • Kalapanidas, E. ve Avouris, N. (2001) Short-term air quality prediction using a case-based classifier Environmental Modelling & Software, 16(3), 263-272.
  • Kermani, E. F., Barani, G. A. ve Hessaroeyeh, M. G. (2018) Cavitation damage prediction on dam spillways using Fuzzy-KNN modeling. J Appl Fluid Mech, 11(2), 323-329.
  • Laden, F., Neas, L. M., Dockery, D. W., ve Schwartz, J. (2000) Association of fine particulate matter from different sources with daily mortality in six US cities, Environmental health perspectives, 108(10), 941-947.
  • Lary, D. J., Lary, T. ve Sattler, B. (2015) Using machine learning to estimate global PM2. 5 for environmental health studies, Environmental health insights, 9, EHI-S15664.
  • Nash, J. E. ve Sutcliffe, J. V. (1970) River flow forecasting through conceptual models part I—A discussion of principles, Journal of hydrology, 10(3), 282-290.
  • Ni, J., Qiao, F., Li, L. ve Di Wu, Q. (2012, July). A memetic PSO based KNN regression method for cycle time prediction in a wafer fab. In Proceedings of the 10th World Congress on Intelligent Control and Automation (pp. 474-478). IEEE.
  • Özcan, H. K., Şahin, Ü., Bayat, C. ve Uçan, O. N. (2006) İstanbul İli Tropsoferik Ozon (O3) Konsantrasyonlarının Hücresel Yapay Sinir Ağ Yöntemiyle Modellenmesi, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 21(2).
  • Yıldırım, Y. ve Bayramoğlu, M. (2006) Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak, Chemosphere, 63(9), 1575-1582.
  • Yüksek, A. G., Bircan, H., Zontul, M. ve Kaynar, O. (2007) Sivas İlinde Yapay Sinir Ağları İle Hava Kalitesi Modelinin Oluşturulması Üzerine Bir Uygulama.
  • Zhang, J. ve Ding, W. (2017) Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong, International journal of environmental research and public health, 14(2), 114.
  • Wu, J.D ve Liu, C.H. (2008) Investigation of engine faultdiagnosis using discrate wavelet transform and neural network. Expert Systems with Applications, 35(3), 1200-1213. doi:10.1016/j.eswa.2007.08.021
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Environmental Engineering
Journal Section Research Articles
Authors

Abdüsselam Altunkaynak 0000-0001-7134-1820

Eyyup Ensar Başakın 0000-0002-9045-5302

Elif Kartal 0000-0003-0877-8776

Publication Date December 31, 2020
Submission Date October 14, 2020
Acceptance Date December 17, 2020
Published in Issue Year 2020 Volume: 25 Issue: 3

Cite

APA Altunkaynak, A., Başakın, E. E., & Kartal, E. (2020). DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 25(3), 1547-1556. https://doi.org/10.17482/uumfd.809938
AMA Altunkaynak A, Başakın EE, Kartal E. DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ. UUJFE. December 2020;25(3):1547-1556. doi:10.17482/uumfd.809938
Chicago Altunkaynak, Abdüsselam, Eyyup Ensar Başakın, and Elif Kartal. “DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25, no. 3 (December 2020): 1547-56. https://doi.org/10.17482/uumfd.809938.
EndNote Altunkaynak A, Başakın EE, Kartal E (December 1, 2020) DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25 3 1547–1556.
IEEE A. Altunkaynak, E. E. Başakın, and E. Kartal, “DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ”, UUJFE, vol. 25, no. 3, pp. 1547–1556, 2020, doi: 10.17482/uumfd.809938.
ISNAD Altunkaynak, Abdüsselam et al. “DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25/3 (December 2020), 1547-1556. https://doi.org/10.17482/uumfd.809938.
JAMA Altunkaynak A, Başakın EE, Kartal E. DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ. UUJFE. 2020;25:1547–1556.
MLA Altunkaynak, Abdüsselam et al. “DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 25, no. 3, 2020, pp. 1547-56, doi:10.17482/uumfd.809938.
Vancouver Altunkaynak A, Başakın EE, Kartal E. DALGACIK K-EN YAKIN KOMŞULUK YÖNTEMİ İLE HAVA KİRLİLİĞİ TAHMİNİ. UUJFE. 2020;25(3):1547-56.

Announcements:

30.03.2021-Beginning with our April 2021 (26/1) issue, in accordance with the new criteria of TR-Dizin, the Declaration of Conflict of Interest and the Declaration of Author Contribution forms fulfilled and signed by all authors are required as well as the Copyright form during the initial submission of the manuscript. Furthermore two new sections, i.e. ‘Conflict of Interest’ and ‘Author Contribution’, should be added to the manuscript. Links of those forms that should be submitted with the initial manuscript can be found in our 'Author Guidelines' and 'Submission Procedure' pages. The manuscript template is also updated. For articles reviewed and accepted for publication in our 2021 and ongoing issues and for articles currently under review process, those forms should also be fulfilled, signed and uploaded to the system by authors.