Modeling of Ozone Interactions with Various Air Pollutants and Meteorological Factors Using Jaya and Teaching-Learning Based Optimization (TLBO) Algorithms

Year 2020, Volume: 8 Issue: 3, 2041 - 2050, 31.07.2020

Nurcan Öztürk

https://doi.org/10.29130/dubited.682602

Cited By: 1

Abstract

Ozone (O3), nitrogen oxides (NOx) and carbon monoxide (CO) concentrations and some meteorological parameters measured hourly have been analyzed to examine the interaction patters between O3 and NOx, CO, air temperature, wind speed, relative humidity, and air pressure by taking into account the diurnal variations of them at urban site (Akçaabat ) in Trabzon. Variations of O3 levels have been modeled via Jaya and Teaching-Learning Based Optimization (TLBO) algorithms considering the effects of certain parameters (NOx and CO concentration, air temperature, wind speed, relative humidity, and air pressure) called as the independent variables. The accuracy of Jaya and TLBO methods has been determined and these methods have been carried out with four different functions: quadratic, exponential, linear and power. Some statistical indices have been applied to evaluate the performance of these models. In conclusion, it is shown that Jaya and TLBO algorithms can be used in the optimization of the regression function coefficients in modelling some air pollutants interactions and the best-fit equation for each parameter is obtained from the quadratic function.

Keywords

Air pollution, Ozone concentration, Modeling

Jaya ve Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmalarını Kullanarak Meteorolojik Faktörler ve Çeşitli Hava Kirleticileri ile Ozon Etkileşimlerinin Modellenmesi

Abstract

Ozon (O3), azot oksitler (NOx) ve karbon monoksit (CO) konsantrasyonları ve saatlik olarak ölçülen bazı meteorolojik parametreler, O3 ile NOx, CO, hava sıcaklığı, rüzgar hızı, bağıl nem ve hava basıncı arasındaki etkileşim eğilimini incelemek için, onların Trabzon'daki kentsel alanda (Akçaabat) günlük değişimlerini dikkate alarak analiz edildi. Bağımsız değişkenler olarak adlandırılan belirli parametrelerin (NOx ve CO konsantrasyonu, hava sıcaklığı, rüzgâr hızı, bağıl nem ve hava basıncı) etkilerini dikkate alarak O3 seviyelerinin değişimleri Jaya ve Öğretme-Öğrenme Tabanlı Optimizasyon (TLBO) algoritmaları ile modellenmiştir. Jaya ve TLBO yöntemlerinin doğruluğu belirlenmiş ve bu yöntemler ikinci dereceden, üstel, doğrusal ve güç olmak üzere dört farklı fonksiyona uygulanmıştır. Bu modellerin başarımını test etmek için bazı istatistiksel belirteçler (ortalama karesel hata, ortalama karesel hatanın karekökü, ortalama mutlak hata, ortalama mutlak yüzde hata ve belirleme katsayısı) kullanılmıştır. Sonuç olarak, Jaya ve TLBO algoritmalarının, bazı hava kirletici etkileşimlerinin modellenmesinde regresyon fonksiyonu katsayılarının optimizasyonunda kullanılabileceği ve her parametre için en uygun denklemin ikinci derece fonksiyonundan elde edildiği görülmüştür.

Keywords

Hava Kirliliği, Ozon konsantrasyonu, Modelleme

References

• [1] Ecolex. (22020, January 5). Regulation on air quality assessment and management [Online]. Available: https://www.ecolex.org/details/legislation/regulation-on-air-quality-assessment-and-management-lex-faoc082742.
• [2] S.C. Pryor, “A case study of emission changes and ozone responses,,” Atmos. Environ., vol. 32, no. 2, pp. 123-131, 1998.
• [3] J.H. Seinfeld and S.N. Pandis, Atmospheric Chemistry and Physic From Air Pollution to Climate Change, USA: John Wiley&Sons, 1998.
• [4] U. Im, M. Tayanç and O. Yenigün, “Analysis of major photochemical pollutants with meteorological factors for high ozone days in İstanbul, Turkey,” Water Air Soil Pollut., vol. 175, pp. 335-359, 2006.
• [5] N. Çetin, B. Bilge Alyüz and Ş. Ayberk, “Trophospheric ozone formation, ist negative effects and current situation in city of Kocaeli,” 21st Engineering and Environmental Problems Symposium, Pennsylvania, 2008.
• [6] U. Im, M. Tayanç and O. Yenigün, “Interaction patterns of major photochemical pollutants in İstanbul, Turkey,” Atmos. Res., vol. 89, pp. 382-390, 2008.
• [7] U. Im, S. Incecik, M. Güler, A. Tek, S. Topcu, Y.S. Unal, O. Yengün, T. Kindap, M.T. Odman and M. Tayanc, “Analysis of surface ozone and nitrogen oxides at urban, semi-rural and rural sites in İstanbul, Turkey,” Sci. Total Environ., vol. 443, pp. 920-931, 2013.
• [8] R. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” Int. J. Ind. Eng. Comput., vol. 7, pp. 19-34, 2016.
• [9] R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems,” Inf. Sci., vol. 183, no. 1, pp. 1-15, 2012.
• [10] B. Akay and D. Karaboga, “A modified artificial bee colony algorithm for real-parameter optimization,” Inf. Sci. (Ny)., vol. 192, pp. 120–142, 2012.
• [11] J. Pierezan and L. Dos Santos Coelho, “Coyote optimization algorithm: A new metaheuristic for global optimization problems,” 2018 IEEE Congr. Evol. Comput. CEC 2018, Brazil, 2018.
• [12] X.S. Yang and S. Deb, “Cuckoo search via Lévy flights” 2009 World Congr. Nat. Biol. Inspired Comput. NABIC 2009, India, 2009.
• [13] A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Comput. Struct., vol. 169, pp. 1–12, 2016.
• [14] P. Civicioglu, “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm,” Comput. Geosci., vol. 46, pp. 229–247, 2012.
• [15] S. Mirjalili, S.M. Mirjalili and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.
• [16] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja and H. Chen, “Harris hawks optimization: Algorithm and applications,” Futur. Gener. Comput. Syst., vol. 97, pp. 849–872, 2019.
• [17] A. Sadollah, H. Sayyaadi and A. Yadav, “A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm,” Appl. Soft Comput. J., vol. 71, pp. 747–782, 2018.
• [18] M.Y. Cheng and D. Prayogo, “Symbiotic organisms search: A new metaheuristic optimization algorithm,” Comput. Struct., vol. 139, pp. 98–112, 2014.
• [19] X. Chen and B. Xu, “Teaching-learning-based artificial bee colony,” Springer International Publishing, 2018.
• [20] P. Civicioglu, E. Besdok, M.A. Gunen and U.H. Atasever, “Weighted differential evolution algorithm for numerical function optimization: A comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms,” Neural Comput. Appl., vol. 5, 2018.
• [21] T.C. Çevre ve Şehircilik Bakanlığı. (2019, 10 Ocak) [Online]. Erişim: https://www.havaizleme.gov.tr/.
• [22] H. T. Kahraman, “Metaheuristic linear modeling technique for estimating the excitation current of a synchronous motor,” Turk J. Elec Eng & Comp Sci., vol. 22, no. 6, pp. 1637-1652, 2014.
• [23] O. Kaplan and E. Celik, “Simplified model and genetic algorithm based simulated annealing approach for excitation current estimation of synchronous motor,” Adv. Electr. Comp. Eng., vol. 18, no. 4, pp.75-85, 2018.
• [24] H. B. Bui, H. Nguyen, Y. Choi, X. N. Bui, T. Nguyen-Thoi and Y. Zandi, “A novel artificial intelligence technique to estimate the gross calorific value of coal based on meta-heuristic and support vector regression algorithms,” Appl. Sci., vol. 9, no. 22, pp. 4868, 2019.
• [25] M. Naderi, E. Khamehchi and B. Karimi, “Novel statistical forecasting models for crude oil price, gas price, and interest rate based on meta-heuristic bat algorithm,” J Petrol Sci Eng., vol. 172, pp. 13-22, 2019.
• [26] K. Sakunthala, S. Iniyan and S. Mahalingam, “Forecasting energy consumption in Tamil Nadu using hybrid heuristic based regression model,” Therm Sci., vol. 23(5 Part B), pp. 2885-2894, 2019.