Yıl: 2021 Cilt: 9 Sayı: 1 Sayfa Aralığı: 45 - 54 Metin Dili: İngilizce DOI: 10.51354/mjen.869736

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

Öz:
With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. The models make the prediction using concentrations of six atmospheric components (PM2.5, PM10, Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO)). The utilized machine learning methods are multilayer perception (MLP), Support Vector Regression (SVM), k-Nearest Neighbor (K-NN), Random Forests (RF), Gradient Boosting (GB), and Elastic Net (EN). After the predictions made by these models, the predicted values were further processed to be classified into one of the six air quality levels defined by United States Environmental Protection Agency. The prediction performances of the models as well as their corresponding classification results were analyzed. It was shown that MLP model gives the lowest RMSE of 2246 for prediction step while SVR achieved the highest accuracy score of 0.790.
Anahtar Kelime:

Konular:
Fen > Mühendislik > Bilgisayar Bilimleri, Yapay Zeka
Fen > Mühendislik > Bilgisayar Bilimleri, Sibernitik
Fen > Mühendislik > Bilgisayar Bilimleri, Bilgi Sistemleri
Fen > Mühendislik > Mühendislik, Elektrik ve Elektronik
Fen > Mühendislik > Mühendislik, Makine
Fen > Mühendislik > Mühendislik, Petrol
Fen > Mühendislik > Jeoloji
Fen > Mühendislik > Malzeme Bilimleri, Biyomalzemeler
Fen > Mühendislik > Polimer Bilimi
Fen > Mühendislik > Malzeme Bilimleri, Kompozitler
Fen > Tıp > Biyokimya ve Moleküler Biyoloji
Fen > Mühendislik > Taşınım Bilimi ve Teknolojisi
Fen > Mühendislik > Nanobilim ve Nanoteknoloji
Fen > Mühendislik > Malzeme Bilimleri, Kâğıt ve Ahşap
Fen > Mühendislik > Enerji ve Yakıtlar
Fen > Mühendislik > Bilgisayar Bilimleri, Donanım ve Mimari
Fen > Mühendislik > Metalürji Mühendisliği
Fen > Mühendislik > Malzeme Bilimleri, Özellik ve Test
Fen > Temel Bilimler > Entomoloji
Fen > Temel Bilimler > Matematik
Fen > Temel Bilimler > Kimya, Analitik
Fen > Temel Bilimler > Kimya, Uygulamalı
Fen > Temel Bilimler > Kimya, Tıbbi
Fen > Temel Bilimler > Kimya, Organik
Fen > Temel Bilimler > Kimya, İnorganik ve Nükleer
Fen > Temel Bilimler > Mineraloji
Fen > Temel Bilimler > Optik
Fen > Temel Bilimler > Taşınım
Fen > Temel Bilimler > Zooloji
Fen > Temel Bilimler > Fizik, Uygulamalı
Fen > Temel Bilimler > Fizik, Matematik
Fen > Temel Bilimler > Fizik, Nükleer
Fen > Temel Bilimler > İstatistik ve Olasılık
Fen > Mühendislik > İnşaat ve Yapı Teknolojisi
Fen > Mühendislik > Hücre ve Doku Mühendisliği
Fen > Mühendislik > Mühendislik, Hava ve Uzay
Fen > Mühendislik > Mühendislik, Kimya
Fen > Mühendislik > Mühendislik, Jeoloji
Fen > Mühendislik > Endüstri Mühendisliği
Fen > Mühendislik > Orman Mühendisliği
Fen > Mühendislik > Malzeme Bilimleri, Seramik
Fen > Mühendislik > Malzeme Bilimleri, Tekstil
Fen > Tıp > Biyoteknoloji ve Uygulamalı Mikrobiyoloji
Fen > Mühendislik > Jeokimya ve Jeofizik
Fen > Mühendislik > Gıda Bilimi ve Teknolojisi
Fen > Temel Bilimler > Biyoloji
Fen > Temel Bilimler > Fizikokimya
Fen > Temel Bilimler > Parazitoloji
Fen > Temel Bilimler > Fizik, Katı Hal
Fen > Temel Bilimler > Fizik, Akışkanlar ve Plazma
Fen > Temel Bilimler > Fizik, Partiküller ve Alanlar
Fen > Mühendislik > Yeşil, Sürdürülebilir Bilim ve Teknoloji
Fen > Mühendislik > Bilgisayar Bilimleri, Yazılım Mühendisliği
Fen > Mühendislik > Bilgisayar Bilimleri, Teori ve Metotlar
Fen > Mühendislik > İnşaat Mühendisliği
Fen > Mühendislik > Çevre Mühendisliği
Fen > Mühendislik > İmalat Mühendisliği
Fen > Mühendislik > Nükleer Bilim ve Teknolojisi
Fen > Mühendislik > Mühendislik, Biyotıp
Fen > Mühendislik > Malzeme Bilimleri, Kaplamalar ve Filmler
Fen > Mühendislik > Maden İşletme ve Cevher Hazırlama
Fen > Temel Bilimler > Ekoloji
Fen > Temel Bilimler > Çevre Bilimleri
Fen > Temel Bilimler > Genetik ve Kalıtım
Fen > Temel Bilimler > Spektroskopi
Fen > Temel Bilimler > Fizik, Atomik ve Moleküler Kimya
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Niall McCarthy, “Air Pollution Contributed to More Than 6 Million Deaths in 2016, Data journalist covering technological”, societal and media topics, 2016.
  • [2] P.Rafaj, G.Kiesewetter , T.Gul, W.Schoppa, J.Cofala, Z.Klimont, P.Purohit, C.Heyes, M.Amann, J.BorkenKleefeld, L.Cozzi. Outlook for clean air in the context of sustainable development goals. : Global Environmental Change, September 2018.
  • [3] OECD (2012), OECD Environmental Outlook to 2050, OECD PUBLISHING. https://dx.doi.org/10.1787/9789264122246-en.
  • [4] Aloke Ghoshal, Pradyut Waghray, George Dsouza, Mahip Saluja, Mayank Agarwal, Ashish Goyal, Sneha Limaye, Akash Balki, Sudhir Bhatnagar, Manish Jain, Sharad Tikkiwal, Abhijit Vaidya, Meena Lopez, Rashmi Hegde, Jaideep Gogtay, “Real-world evaluation of the clinical safety and efficacy of fluticasone/formoterol FDC via the Revolizer in patients with persistent asthma in India”, On 25 November 2019, 10.1016/j.pupt.2019.101869.
  • [5] Burden of disease from ambient air pollution for 2016, 1211 Geneva 27, World Health Organization 2018. https://www.who.int.
  • [6] World Health Organization, Ambient air pollution: a global assessment of exposure and burden of disease, 2016, https://apps.who.int/iris/handle/10665/250141.
  • [7] X. Li, Ling Jin, and H. Kan, Air pollution: a global problem needs local fixes, 25 JUNE 2019, china, https://doi.org/10.1038/d41586-019-01960-7.
  • [8] Blondeau, P., Iordache, V., Poupard, O., Genin, D., Allard, F., 2005. Relationship between outdoor and indoor air quality in eight French schools. Indoor Air 15, 2–12, 10.1111/j.1600-0668.2004.00263.
  • [9] Brian S. Freeman, G. Taylor, B. Gharabaghi, and Jesse Thé, forecasting air quality time series using deep learning, 24 May 2018. https://doi.org/10.1080/10962247.2018.1459956.
  • [10]Nesreen K. Ahmed, Amir F. Atiya , N.El Gayar &H. ElShishiny, An Empirical Comparison of Machine Learning Models for Time Series Forecasting, 15 Sep 2010. https://doi.org/10.1080/07474938.2010.481556.
  • [11]Ping-Feng Pai, Kuo-Ping Lin, Chi-Shen Lin, and PingTeng Chang, Time series forecasting by a seasonal support vector regression model, June 2010, https://doi.org/10.1016/j.eswa.2009.11.076.
  • [12]Francisco S. de Albuquerque Filho, Francisco Madeiro e Sérgio M. M. Fernandes, Paulo S. G., de Mattos Neto, and Tiago A. E. Ferreira, Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks, Paulo 2013. http://dx.doi.org/10.1590/S0100-40422013000600007.
  • [13]James R. Lloyd, GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes, 16 August 2013. https://doi.org/10.1016/j.ijforecast.2013.07.002.
  • [14]H.Tyralis, and G.Papacharalampous, Variable Selection in Time Series Forecasting Using Random Forests, 4 October 2017. https://doi.org/10.3390/a10040114.
  • [15]M. M. Dedovic, S. Avdakovic, I. Turkovic, N. Dautbasic, and T. Konjic, Forecasting PM10 concentrations using neural networks and system for improving air quality, 08 December 2016, 10.1109/BIHTEL.2016.7775721.
  • [16]Bing-Chun Liu, A. Binaykia, P. Chang, M.K. Tiwari, C.- C. Tsao, urban air quality forecasting based on multidimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang. July 14, 2017, https://doi.org/10.1371/journal.pone.0179763.
  • [17]H.Zheng ,H. Li, X. Lu, and T. Ruan, A Multiple Kernel Learning Approach for Air Quality Prediction, 12 Jun 2018, https://doi.org/10.1155/2018/3506394.
  • [18]F. Martínez, M. P. Frías, F. Charte and A. J. Rivera, Time Series Forecasting with KNN in R: the tsfknn Package, December 2019, ISSN 2073-4859.
  • [19]K.Maheshwari and S. Lamba, Air Quality Prediction using Supervised Regression Model, 03 February 2020, 10.1109/ICICT46931.2019.8977694.
  • [20]Fang Shen, Jing Liu, and Kai Wu, Multivariate Time Series Forecasting based on Elastic Net and High-Order Fuzzy Cognitive Maps: A Case Study on Human Action Prediction through EEG Signals, 29 May 2020. 10.1109/TFUZZ.2020.299851.
  • [21]World's Air Pollution: Real-time Air Quality Index, http://waqi.info/.
  • [22]Eusebio Jarauta-Bragulat, Carme Hervada-Sala, Juan Jose Egozcue, Air Quality Index Revisited from a Compositional Point of View, published online: 23 May 2015 © International Association for Mathematical Geosciences 2015 .
  • [23]A. Sanjivanrao More, D.Sunil Ranaware, B. D. Wamane, and G. S. Salunkhe, Enhancement in Financial Time Series Prediction with Feature Extraction in Text Mining Techniques, Nov 2019, 2395-0056, International Research Journal of Engineering and Technology (IRJET).
  • [24]V. N. Vapnik, An overview of statistical learning theory, Sept. 1999.10.1109/72.788640.
  • [25]N. I. Pankevych, R.Sankar, Time Series Prediction Using Support Vector Machines: A Survey, 24 April 2009. 10.1109/MCI.2009.932254.
  • [26]M. Awad, R.Khanna, Efficient Learning Machines (chapter: Support Vector Regression, Pages 67-80), 27 April 2015. https://doi.org/10.1007/978-1-4302-5990-9
  • [27]F.Martínez, M. P. Frías, M. D. Pérez, and A. J. Rivera, a methodology for applying k-nearest neighbor to time series forecasting, 21 NOV 2019. https://doi.org/10.1007/s10462-017-9593-z.
  • [28]J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen, M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall, and G.Cawle, extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki, 22 Aug 2003. https://doi.org/10.1016/S1352- 2310(03)00583-1.
  • [29]S. Touzani, J. Granderson, and S. Fernandes, Gradient boosting machine for modelling the energy consumption of commercial buildings, Nov 2017. https://doi.org/10.1016/j.enbuild.2017.11.039.
  • [30]Max Kuhn, Kjell Johnson, Applied Predictive Modeling, New York 2013, https://doi.org/10.1007/978-1-4614- 6849-3.
  • [31]Hui Zou, and Trevor Hasti, Regularization and variable selection via the elastic net, 09 March 2005, https://doi.org/10.1111/j.1467-9868.2005.00503.x.
  • [32]P. García Nieto, F. Sánchez Lasheras, E. García-Gonzalo, F. de Cos Juez, PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study, 2018, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2017.11.291.
  • [33]Z. Meng, Ground Ozone Level Prediction Using Machine Learning, 2019, Journal of Software Engineering and Applications, 10.4236/jsea.2019.1210026.
  • [34]R. Waman Gore, D. S. Deshpande, An approach for classification of health risks based on air quality levels, 01 December 2017, India, 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), 10.1109/ICISIM.2017.8122148.
  • [35]S. Agarwal, S. R. Sharma, Md H. Suresh Rahman, et al., Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions, 2020, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2020.139454.
  • [36]Doreswamy, K. Harish Kumar, Y. Km, I. Gad, Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models, 2020, Procedia Computer Science, https://doi.org/10.1016/j.procs.2020.04.221.
  • [37]S. Ameer, M. Ali Shah, A. Khan; H. Song, C. Maple, S. Ul Islam, M. N. Asghar, Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities, 2019, 10.1109/ACCESS.2019.2925082.
  • [38]W. Leong, R. Kelani, Z. Ahmad, Prediction of air pollution index (API) using support vector machine (SVM), 2020, Journal of Environmental Chemical Engineering, https://doi.org/10.1016/j.procs.2020.04.221.
  • [39]Z. Yu, and Z. Niu; W. Tang, Deep Learning for Daily Peak Load Forecasting—A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping, 29 January 2019, 10.1109/ACCESS.2019.2895604.
  • [40]C. S. Malley, D. K. Henze, Johan C.I. Kuylenstierna, H. W. Vallack, Y. Davila, S. C. Anenberg, M. C. Turner, and M. R. Ashmore, Updated Global Estimates of Respiratory Mortality in Adults ≥30 Years of Age Attributable to Long-Term Ozone Expos, 28 August 2017, https://doi.org/10.1289/EHP1390.
  • [41]Nan-Hung Hsieh, Chung-Min Liao, Fluctuations in air pollution give risk warning signals of asthma hospitalization, August 2013, https://doi.org/10.1016/j.atmosenv.2013.04.043.
  • [42]S. Du, T. Li, and Shi-Jinn Horng,Time Series Forecasting Using Sequence-to-Sequence Deep Learning Framework, 02 May 2019, 10.1109/PAAP.2018.00037.
  • [43]T.Liu, A. K. H. Lau, K. Sandbrink, J. C. H. Fung,Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong, 24 March 2018, https://doi.org/10.1002/2017JD028052.
  • [44]NICOLÒ BALDON, Time series Forecast of Call volume in Call Centre using Statistical and Machine Learning Methods, Sweden 2019, urn:nbn:se:kth:diva-265002.
  • [45]S. G. Gocheva-Ilieva, A. V. Ivanov, D. S.Voynikova, and D. T. Boyadzhiev, Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach, 25 September 2013, https://doi.org/10.1007/s00477-013-0800-4.
  • [46]G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning algorithms: a Multiple-Case Study from Greece, 29 November 2018, https://doi.org/10.1007/s11269-018- 2155-6.
APA MAHMOOD W, AVŞAR E (2021). One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level. Manas Journal of Engineering, 9(1), 45 - 54. 10.51354/mjen.869736
Chicago MAHMOOD Waleed Khalid M.,AVŞAR ERCAN One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level. Manas Journal of Engineering 9, no.1 (2021): 45 - 54. 10.51354/mjen.869736
MLA MAHMOOD Waleed Khalid M.,AVŞAR ERCAN One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level. Manas Journal of Engineering, vol.9, no.1, 2021, ss.45 - 54. 10.51354/mjen.869736
AMA MAHMOOD W,AVŞAR E One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level. Manas Journal of Engineering. 2021; 9(1): 45 - 54. 10.51354/mjen.869736
Vancouver MAHMOOD W,AVŞAR E One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level. Manas Journal of Engineering. 2021; 9(1): 45 - 54. 10.51354/mjen.869736
IEEE MAHMOOD W,AVŞAR E "One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level." Manas Journal of Engineering, 9, ss.45 - 54, 2021. 10.51354/mjen.869736