نشریه محیط زیست طبیعی

مقایسه دقت مدل رگرسیون بردار پشتیبان با دو روش رایج شبکه‌ عصبی مصنوعی و شبکه عصبی فازی- تطبیقی در پیش بینی غلظت آلاینده PM10

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران

2 دانشیار محیط زیست، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد،

3 استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس

4 دانشیار محیط زیست، دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران

چکیده

شهر یزد واقع در مرکز ایران می باشد که در اکثر ایام سال طوفان های گرد و غبار را تجربه می کند، و همچنین طی دو دهه گذشته شاهد رشد سریع صنعتی بخصوص در صنایع با توان غبار زایی بالا (کاشی، فولاد و ..) بوده است، لذا پیش بینی غلظت آلاینده ذرات از طریق بکارگیری سیستم های پیش آگاهی دقیق در زمان طوفان های گرد و غبار و آلودگی های صنعتی برای حفظ بهداشت و سلامت شهروندان بسیار حیاتی است. در این مطالعه بطور همزمان از مدل شبکه عصبی فازی تطبیقی (ANFIS)، رگرسیون بردار پشتیبان (SVR) و شبکه‌ عصبی مصنوعی (ANN) در پیش بینی میزان غلظت آلاینده PM10 برای 24 ساعت آینده و معرفی دقیق ترین روش استفاده شد. به همین منظور داده های PM10 شهر یزد به همراه پارامتر هواشناسی شهر در بازه زمانی سال های 1394 تا 1398 مورد استفاده قرار گرفت. نتایج این تحقیق نشان داد که مدل ANFIS با (R2=0.989) و دقتی در حدود 99 درصدی بهتر از سایر مدل ها در این حالت است و بعد از آن به ترتیب مدل ANN با (R2=0.978) و SVR با (R2=0.957) دارای بهترین دقت بودند. بنابراین می توان گفت، با توجه به صحت مدل ، از این مدل می توان برای پیش بینی غلظت آلاینده PM10 استفاده کرد و این امر می تواند مسئولین را در تصمیم گیری های به موقع در جهت حفظ سلامت عمومی قبل از شروع وضعیت هشدار غلظت آلاینده PM10 کمک کند.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of the accuracy of the support vector regression model with two common methods of artificial neural network and adaptive neuro-fuzzy inference system in predicting the pollutant concentration of the PM10

نویسندگان [English]

  • Seyyed Mostafa Mirzadeh 1
  • Farhad Nejadkoorki 2
  • Vahid Moosavi 3
  • Seyyed Abolghasem Mirhoseini 4

1 Department of Agriculture, College of Engineering, Yazd Branch, Yazd Islamic Azad University, Yazd, Iran

2 Associate Professor of Environmental Science, School of Natural Resources and Desert Studies, Yazd University.

3 Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran

4 Department of Agriculture, College of Engineering, Yazd Branch, Yazd Islamic Azad University, Yazd, Iran

چکیده [English]

Abstract

The city of Yazd has located in central of Iran that experiences dust storms most of the year, and also has witnessed rapid industrial growth, especially in industries with high dust generation capacity (tiles, steel, etc.) during the last two decades. Therefore, predicting the concentration of particulate pollutants during dust storms and industrial pollution through the use of accurate warning systems is critical to maintaining the health of citizens. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and Artificial Neural Network (ANN) models were used at the same time to predict the concentration of PM10 for the next 24 hours and to introduce the most accurate method. For this purpose, were used PM10 data, meteorological parameters, and gas pollutant concentrations of Yazd city from 2015 to 2019 years. The results of this study showed that the ANFIS model with (R2 = 0.989) and accuracy of about 99% were better than other models, followed by the ANN model with (R2 = 0.978) and the SVR model with (R2 = 0.957) had the best accuracy, respectively. Finally, data analysis showed that at the operational scale, city managers can make appropriate and timely decisions based on high-precision predictive models, maintain public health before the start of the PM10 alert status.

کلیدواژه‌ها [English]

  • PM10
  • 24-hour prediction
  • Adaptive Neuro-Fuzzy Inference System
Adar, Sara D., Filigrana, Paola A., Clements, Nicholas, & Peel, Jennifer L. (2014). Ambient Coarse Particulate Matter and Human Health: A Systematic Review and Meta-Analysis. Current Environmental Health Reports, 1 (3), 258-274.
Amanollahi, Jamil, & Ausati, Shadi. (2020). Validation of linear, nonlinear, and hybrid models for predicting particulate matter concentration in Tehran, Iran. Theoretical and Applied Climatology, 140(1), 709-717.
Azadeh, A., Saberi, M., Anvari, M., Azaron, A., & Mohammadi, M. (2011). An adaptive network based fuzzy inference system–genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants. Expert Systems with Applications, 38(3), 2224-2234. doi:
Buragohain, Mrinal, & Mahanta, Chitralekha. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1), 609-625.
EPA, US. (2016). Health and Environmental Effects of Particulate Matter (PM).  Retrieved from https://www.epa.gov/pm-pollution/health-and-environmental-effects-particulate-matter-pm.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., . . . Van Dorland, R. (2007). Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2. United Kingdom: Cambridge University Press.
Ghasemi, Afsaneh, & Amanollahi, Jamil. (2019). Integration of ANFIS model and forward selection method for air quality forecasting. Air Quality, Atmosphere & Health, 12(1), 59-72. doi: 10.1007/s11869-018-0630-0
Hamanaka, R. B., & Mutlu, G. M. (2018). Particulate Matter Air Pollution: Effects on the Cardiovascular System. Front Endocrinol (Lausanne), 9, 680.
Haykin, Simon. (1994). Neural networks: a comprehensive foundation: Prentice Hall PTR.
Jang, J-SR. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Kaboodvandpour, Shahram, Amanollahi, Jamil, Qhavami, Samira, & Mohammadi, Bakhtiyar. (2015). Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran. Natural Hazards, 78(2), 879-893.
Kim, H. S., Park, I., Song, C. H., Lee, K., Yun, J. W., Kim, H. K., . . . Han, K. M. (2019).
Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model. Atmos. Chem. Phys., 19(20), 12935-12951.
Kim, J. (2019). Particulate Matter Mortality Rates and Their Modification by Spatial Synoptic Classification. Int J Environ Res Public Health, 16(11).
Lawrence, Jeannette. (1994). Introduction to Neural Networks: Design. Theory, and Applications (California Scientific Software, Nevada City, CA).
Liu, Huixiang, Li, Qing, Yu, Dongbing, & Gu, Yu. (2019). Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms. Applied Sciences, 9, 4069.
Maleki, Heidar, Sorooshian, Armin, Goudarzi, Gholamreza, Baboli, Zeynab, Tahmasebi Birgani, Yaser, & Rahmati, Mojtaba. (2019). Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy. doi: 10.1007/s10098-019-01709-w
Omidvarborna, Hamid, Kumar, Ashok, & Kim, Dong-Shik. (2015). Recent studies on soot modeling for diesel combustion. Renewable and Sustainable Energy Reviews, 48, 635-647.
Paschalidou, Anastasia K., Karakitsios, Spyridon, Kleanthous, Savvas, & Kassomenos, Pavlos A. (2011). Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environmental Science and Pollution Research, 18(2), 316-327.
Pérez, Noemí, Pey, Jorge, Cusack, Michael, Reche, Cristina, Querol, Xavier, Alastuey, Andrés, & Viana, Mar. (2010). Variability of Particle Number, Black Carbon, and PM10, PM2.5, and PM1 Levels and Speciation: Influence of Road Traffic Emissions on Urban Air Quality. Aerosol Science and Technology, 44(7), 487-499.
Schlink, Uwe, Dorling, Stephen, Pelikan, Emil, Nunnari, Giuseppe, Cawley, Gavin, Junninen, Heikki, . . . Doyle, Martin. (2003). A rigorous inter-comparison of ground-level ozone predictions. Atmospheric Environment, 37(23), 3237-3253.
Scholkopf, Bernhard, Sung, Kah-Kay, Burges, Christopher JC, Girosi, Federico, Niyogi, Partha, Poggio, Tomaso, & Vapnik, Vladimir. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE transactions on Signal Processing, 45(11), 2758-2765.
Smola, Alex J, & Schölkopf, Bernhard. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
Vapnik, Vladimir. (2013). The nature of statistical learning theory: Springer science & business media.
Wasley, Andrew; , Heal, Alexandra;, Harvey, Fiona;, & Lainio, Mie (2019). Revealed: UK government failing to tackle rise of serious air pollutant. The Guardian.
World Health, Organization. (2016). Ambient air pollution: a global assessment of exposure and burden of disease. Geneva: World Health Organization.
World Health, Organization. (2017). Evolution of WHO air quality guidelines: past, present and future (pp. 39): Copenhagen: WHO Regional Office for Europe.
Yadav, V., & Nath, S. (2019). Novel hybrid model for daily prediction of PM10 using principal component analysis and artificial neural network. International Journal of Environmental Science and Technology, 16(6), 2839-2848. doi: 10.1007/s13762-018-1999-x
Zhang, G. Peter. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
نشریه محیط زیست طبیعی
دوره 74، شماره 1
خرداد 1400
صفحه 167-179