Fatma KUNT, Zeynep Cansu AYTURAN, Sukru DURSUN

Used Some Modelling Applications in Air Pollution Estimates

Air Pollution is produced by airborne Sulphur dioxide (SO2), particulate matter (PM), nitrogen oxides (NOx) and ozone (O3) of pollutants in the environment and defined as the level that will have a negative impact on human health. This pollution disrupts natural processes in the atmosphere and affects public health and comfort. In the developing world, industry and human population growth poses a risk in terms of environmental pollution. Therefore, it is important to estimate air pollution and measures taken in advance. Some modelling applications used for this purpose include the commonly used Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System models. In this study; compared different modelling programs with some gases which cause air pollution were estimated. The results were compared and try to select the most suitable modelling program.

Keywords:

Air pollution modelling, ANN, ANFIS,

PDF

___

Akkoyunlu A., Yetilmezsoy K., Erturk F, Oztemel E, (2010) A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area, Int. J. Environ. & Pollution, 40, 301-321.
Asadollahfardi G., Zangooei H, Aria SH., (2016) Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City, Asian J. Atmos. Environ., 10, 67-79.
Basheer IA, Hajmeer M, (2000) Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol.l Meth., 43, 3-31.
Battiti R, (1992) First and second order methods for learning between steepest descent and Newton’s method, Neural Comput. 4, 141–166.
Bonissone P, (2002), Adaptive Neural Fuzzy Inference Systems (ANFIS): Analysis and Applications, Online avalible from https://www.researchgate.net/file.PostFileLoader.html?id.
Boube .R, Fox DL, Turner DB, Stern AC, (1994) Fundamentals of Air Pollution, 3rd Edition, Elsevier, USA, 3-150,.
Chelani AB, Chalapati Rao CV, Phadke KM, (2001) Hasan M. Z, Prediction of sulphur dioxide concentration using artiﬁcial neural networks, Environ. Model. & Software, 17, 161–168.
Curtis LW, Rea P, Smith-Willis E, Fenyves PY, (2006) Adverse health effects of outdoor air pollutants. Environ Int., 32, 815–830. Dehkordi MB, (2012) Compressive Sensing Based Compressed Neural Network for Sound Source Localization, Am. J. Intel. Syst., 2, 35-39. Demir G, Altay GC, Sakar O, Albayrak S, Ozdemir H, Yalcin S, (2008) Prediction and evaluation of tropospheric ozone concentration in Istanbul using artificial neural network modeling according to time parameter, J. Sci. & Ind. Res., 67, 674-679.
Dib S, Ferdi B, Benachaiba C, (2011) Adaptive Neuro-Fuzzy Inference System based DVR Controller Design, Online avalible from (http://lejpt.academicdirect.org/A18/get_htm.php?htm=049_064).
Dursun S, Kunt F, Taylan O, (2015) Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors, Int. J. Environ. Sci. & Tech., 12, 3915-3928.
Fuller AD, (1995) Neural fuzzy systems. Abo Akademi University, Abo.
Gardner MW, Dorling SR, (1998) Artificial neural networks: the multilayer perceptron: a review of applications in atmospheric sciences, Atmos. Environ. 32, 2627–2636.
Hanafy TOS, Al-Osaimy AS, Merabtine N, (2015) Identification and Modeling of Air Pollutions using Adaptive Nero Fuzzy Systems (ANFIS), Int. J. Comp. & Inform. Tech. 4, 2.
Haykin S, (2001) Neural Networks: A Comprehensive Foundation, 2nd Ed. Pearson Education Inc., New Delhi, India.
Hornik K, Stinchcombe M, White H, (1989) Multilayer feedforward networks are universal approximators, Neural Netw. 2, 359–366.
Hussain ST, (2011) Sulfur Dioxide: Properties, Applications and Hazards, Nova Science Publishers, Inc., Chapter 3, 49-68.
Jang JSR, Sun CT, Mizutani E, (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, NJ, p 587.
Moumeni B, Golmai SH, Palangi JA, (2013) Comparison of using different systems of artiﬁcial intelligence in subsurface water level prediction (case study: paddy ﬁelds of plain areas between Tajan and Nekaroud Rivers, Mazandaran, Iran). J Novel Appl Sci., 2, :375–381. Ocak S, Erturk F, (2009) An Air Quality Model and Its Evaluation in Erzurum, Turkey, J. Int. Environ. Appl. & Sci., 4, 454-465.
Özgana E, Uzunoğlu M, Kap T, (2009) Prediction of The Effect of The Vibration on The Physical and Mechanical Properties of Concrete Based on Adaptive Neuro-Fuzzy Inferences System, A Region Concrete Application, 5. Uluslararası İleri Teknolojiler Sempozyumu (İATS’09).
Ozturk ZC, Dursun S, (2016) Modelling of Atmospheric SO2 Pollution in Seydişehir Town by Artificial Neural Networks. J. Int. Environ. Appl. & Sci., 11, 1-7. Ozturk ZC, Kunt F, Dursun S, (2015) Application of Artificial Neural Network in Environmental Engineering Problems, International Conference of Ecosystems (ICE2015), 368-375.
Pham DT, Pham PTN, (1999) Artificial intelligence in engineering, Int. J. Mach. Tools & Manuf., 39, 937-949.
Polat K., (2011) A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS), Neural Comput & Applic DOI 10.1007/s00521-011-0602-x.
Prasad K, Gorai AK, Goyal P, (2016) Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time, Atmos. Environ., 128, 246-262.
Rafatia L, Ehrampousha MH, Talebib A, Mokhtari M, Kherad Pisheh Z, Dehghan HR, (2014) Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case study: city of Yazd, Iran), Desert 19, 131-135.
Rawat K, Burse K, (2013) A soft computing genetic-neuro fuzzy approach for data mining and its application to medical diagnosis. Int J Eng Adv Technol., 3, 409–411.
Rozlach Z, (2015) Data-driven Modelling in River Channel Evolution Research: Review of Artificial Neural Networks, J. Int. Environ. Appl. & Sci. 10, 384-398.
Rumelhart DE., McClelland JL, (1995) Parallel Distributed Processing: Explorations in the Microstructure of Cognitions, Vol.1. MIT Press, Cambridge, England.
Sarle W, (1997) Neural network frequently asked questions, Online avalible from ftp://ftp.sas.com/pub/neural/FAQ.html.
Sauter GD, (1976) Generic Survey of Air Quality Simulation Models, Proceedings of the Conference on Environmental Modeling and Simulation, EPA 600/9-76-016.
Savić M, Mihajlović I, Živković Ž, (2013) An ANFIS-Based Air Quality Model for Prediction of SO2 Concentration in Urban Area, Serbian Journal of Management 8, 25- 38.
Taylan O, (2013), Assessing air quality in Jeddah by modeling suspended PM10 concentration. J Int Environ Appl Sci 8:326–335.
URL 1 Ambient Air Pollution Monitoring, Stack Monitoring: techniques & instrumentation, Experimental analysis: Gaseous & particulates; standards & limits, Online avalible from (http://nptel.ac.in/courses/Webcoursecontents/IITDelhi/Environmental%20Air%20Pollution/air%20pollution%20(Civil)/Module-2/1.htm#lec2.)
Wang SC, (2003) Artificial Neural Network, The Springer Int. Series in Eng. & Computer Science, 743, 81-100. Wasserman PD, (1989) Neural Computing. Theory and Practice, Van Nostrand Reinhold, NY.
Yechena Q, Rezab L, Lianga G, (2015) A new modeling algorithm based on ANFIS and GMDH, J. Intelligent & Fuzzy Systems, 29, 1321-1329.
Yıldırım Y, Bayramoglu M, (2006) Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. Chemosphere 63, 1575–1582.
Zannetti P, (1990) Air Pollution Modeling Theories, Computational Methods and Available Software, Springer Science + Business Media, LLC, AeroVironment Inc. Monrovia, California, 3-20.