دوره 17، شماره 47 - ( 10-1396 )                   جلد 17 شماره 47 صفحات 59دوره39فصل__Se | برگشت به فهرست نسخه ها

XML English Abstract Print


1- دانشگاه خوارزمی تهران ، asadollahfardi@yahoo.com
2- دانشگاه آزاد اسلامی واحد جنوب تهران
چکیده:   (5384 مشاهده)
مدیریت ذرات معلق یکی از موارد مهم در کنترل آلودگی شهرها محسوب می­شود. این ذرات باعث ایجاد و توسعه بیماری های قلبی و تنفسی مختلف در افراد می­گردد. شهر مشهد به عنوان یکی از شهرهای اصلی و پرجمعیت ایران با توجه به شرایط اقلیمی و همچنین توریستی بودن، بیش از هر چیزی در معرض خطر این نوع آلودگی قرار دارد. در این تحقیق سعی شده با استفاده از مدل­های پرسپترون شبکه های عصبی مصنوعی و مدل زنجیره مارکوف غلظت PM10 پیش­بینی و تحلیل گردد. برای این منظور از داده­های ساعتی CO، SO2، PM2.5 و دما برای دو ماه فروردین و اردیبهشت در سال 1394 استفاده شد. از مجموع 1488 سری داده، 1300 داده برای آموزش شبکه و 188 داده جهت صحت­سنجی استفاده گردید. نتایج نشان­دهنده عملکرد مطلوب این روش­ها در پیش­بینی میزان آلاینده و همچنین احتمال وقوع ساعات با کیفیت­­های مختلف آلودگی بود. بهترین مدل پرسپترون میزان آلاینده ذرات معلق را با ضریب همبستگی 88/0، شاخص تطابق 91/0، میانگین بایاس خطای 0874/0 و جذر میانگین مربعات خطای 26/2 پیش­بینی نمود، همچنین مدل مارکوف با خطای مطلق متوسط حدود 1/0 درصد احتمالات انتقال وضعیت و تداوم وضعیت­های مختلف آلودگی هوای ناشی از ذرات معلق را پیش­بینی نمود.
 
 
 
 
 
 
 
 
متن کامل [PDF 1204 kb]   (2976 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: اب و هواشناسی
دریافت: 1395/6/24 | پذیرش: 1396/8/21

فهرست منابع
1. اکبری، م.، محمدی، ح.، شمسی‌پور، ع. (1393) بررسی تغییرات شاخص‌های دینامیکی همزمان با توفان‌های حوضه آبریز کارون، نشریه علمی- پژوهشی جغرافیا و برنامه‌ریزی، شماره 48.
2. سلیمی، س.، خسروانی، و.، اکبری، م. (1392) بررسی نقش الگوهای سینوپتیکی جوی برآلودگی هوا (مطالعه موردی آلودگی شدید هوای شهر تهران در روزهای 16-12 آذرماه 1391)، اولین همایش سراسری محیط زیست، انرژی و پدافند زیستی، تهران.
3. علیجانی، ب. (1381) شناسایی تیپ‌های هوایی باران‌آور تهران بر اساس محاسبه چرخندگی، مجله تحقیقات جغرافیایی، شماره 36 و 64.
4. فتاحی، ا، حجازی‌زاده، ز. (1385) تحلیل زمانی مکانی توده‌های هوا و کاربرد آن در پایش دوره‌های خشک و مرطوب در حوضه‌های جنوب غربی ایران، فصلنامه تحقیقات جغرافیایی، شماره 81،.
5. فرج‌زاده اصل، م.، محمدی، ع. (1390) پهنه‌بندی آسیب‌پذیری آب‌های زیرزمینی با کمک الگوریتم‌های فازی عصبی (مطالعه موردی: استان زنجان)، مجله سنجش از راه دور و GIS ایران، سال سوم شماره اول.
6. کاویانی، م.، علیجانی، ب. (1390) مبانی آب و هواشناسی، انتشارات سازمان مطالعه و تدوین کتب علوم انسانی دانشگاهها (سمت).
7. Asadollahfardi, G.R., Zangooei, H., Aria, S.H., Danesh, E. (2017). Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam, Environmental Quality Management. 26 (3), (pp. 55-72).
8. Barrero, M.A., Grimalt, J.O., Canto'n L. (2006). Prediction of daily ozone concentration maxima in the urban atmosphere. Chemom Intell Lab Syst., 80, (pp. 67–76).
9. Bordignon, S., Gaetan, C., Lisi, F. (2002). Nonlinear models for groundlevel ozone forecasting. Stat Meth Appl., 11:2, (pp. 27–46).
10. Caputo, M., Gimenez, M., Schlamp, M. (2003). Intercomparison of atmospheric dispersion models, Atmos. Environ. 37, (PP. 2435–2449).
11. Chan C.K. and Yao, X. (2008). Air pollution in mega cities in China, Atmospheric Environment, vol. 42, no. 1, (pp. 1–42).
12. Chattopadhay, S. and Chattopadhay, G. (2012). Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis, Pure Appl. Geophys. 169, (pp. 1891–1908).
13. Chen, C., Lin, C.H., Long, Z., Chen, Q. (2014). Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method, Indoor Air, 24, (pp. 81-92).
14. Chen, C., Liu, W., Lin, C.H., Chen, Q. (2015). A Markov chain model for predicting transient particle transport in enclosed environments, Building and Environment, 90, (pp. 30-36).
15. Chung, K.L., AitSahlia, F. (2003), Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance, Springer Undergraduate Texts in Mathematics and Technology, ISSN 0172-6056.
16. Cohen, S., Intrator, N. (2002). Automatic model selection in a hybrid perceptron/ radial network, Information Fusion: Special Issue on Multiple Experts, 3(4), (pp. 259–266).
17. Delnavaz, M., Zangooei, H., Zangooei, M. (2016). Application of Mathematical Models and Fuzzy Regression Analysis to Determine the Microbial Growth Kinetic Coefficients and Predicting Quality of Treated Wastewater, Nature Environment and Pollution Technology. 15 (1), 43.
18. Deng, X., Zhang, F., Rui W. (2013). PM2.5-induced oxidative stress triggers autophagy in human lung epithelial A549 cells, Toxicology in Vitro, vol. 27, no. 6, (pp. 1762–1770).
19. Du, X., Kong, Q., Ge, W., Zhang, S., Fu, L., (2010). Characterization of personal exposure concentration offine particles for adults and children exposed to high ambient concentrations in Beijing. China J. Environ. Sci. 22, (pp. 1757-1764).
20. Eleuteri, A., Tagliaferri, R., Milano, L. (2005). A novel information geometric approach to variable selection in MLP networks. Neural Netw, 18(10):130, (pp. 9–18).
21. Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J. (2015). Artificial neural networks forecasting of PM2.5pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, (pp. 118-128).
22. Goss, C.H., Newsom, S.A., Schildcrout, J.S., Sheppard, L. and Kaufman, J.D. (2004). Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis, American Journal of Respiratory and Critical Care Medicine, vol. 169, no. 7, (pp. 816–821).
23. Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D. (2007). Uncertainties in air toxics calculated by the dispersion models AERMOD and ISCST 3 in the Houston ship channel area, J. Appl. Meteorol. Climatol. 46, (pp.1372–1382).
24. Harsham, D.K., Bennett, M. (2008). A sensitivity study of validation of three regulatory dispersion models, Am. J. Environ. Sci. 4, (pp. 63–76).
25. Jones RM, (2014). Nicas M. Benchmarking of a Markov multizone model of contaminant transport, Ann Occup Hyg, 58:10, (pp. 18-31).
26. Kalapanidas, E., Avouris, N. (2001). Short-term air quality prediction using a case-based classifier, Environ Modell Softw.,16:2, (pp. 63–72).
27. Karatzas, K., Kaltsatos, S. (2007). Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul Model Pract Theory, 15(10):131, (pp. 0–9).
28. Kohavi, R., John, G.H. (1997). Wrappers for feature subset selection. Artif Intell., 97, (pp. 273–324).
29. Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M. (2003). 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, Atmos Environ., 37(32), (pp. 39–50).
30. Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York, USA.
31. Kurt, A., Gulbagci, B., Karaca F., Alagha, O. (2008). An online air pollution forecasting system using neural networks, Environment International, 34, (pp. 592–598).
32. Logofet, D.O., Lensnaya, E.V. (2000). The mathematics of Markov models: what Markov chains can really predict in forest successions. Ecol Modell., 2:3, (pp. 285–98).
33. Moustris, K.P., Ziomas I.C. and Paliatsos A.G. (2010). 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 using a neural Networks in Athens, Greece, Water Air Soil Pollut, 209, (pp. 29–43).
34. Moustris, K.P. Larissi, I.K. Nastos P.T. Koukouletsos K.V. and Paliatsos, A.G. (2013). Development and application of artificial neural network modeling in forecasting PM10 Levels in a Mediterranean City, Water air soil pollut. 224: 1634, (pp. 3-11).
35. Nagendra, S.M., Khare, M. (2006). Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions, Ecol Modell, 190, (pp. 99-115).
36. Nicas M. (2000). Markov modeling of contaminant concentrations in indoor air, AIHAJ 61:48.
37. Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J. (2005). Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations, Atmos Environ., 39:65, (pp. 24–36).
38. Niska, H., Heikkinen, M., Kolehmainen, M. (2006). Genetic algorithms and sensivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series, Lect Notes Comput Sci., 4224:2, (pp. 24–31).
39. Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E., and Fila, M. (2006). Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques. Chemometrics and intelligent laboratory systems, 83(1), (pp. 26-33).
40. Qiu, H., Yu, I., Wang, X., Tian, L., Tse, L.A., Wong, T.W., (2013). Differential effects offine and coarse particles on daily emergency cardiovascular hospitalizations in Hong Kong. Atmos. Environ., 64, (pp. 296-302).
41. Rumelhart; D.E., Clelland; J.L.M. (1986). Parallel distribution processing: Exploration in the microstructure of cognition, Cambridge, MA: MIT Press (p. 1).
42. Shamshad, A., Bawadi, M.A., Wan Hussin, W.M.A., Majid, T.A., Sanusi, S.A.M. (2005). First and second order Markov chain models for synthetic generation of wind speed time series, Energy, 30, (pp. 693–708).
43. Slaughter, J.C., Lumley, T., Sheppard, L., Koenig, J.Q., and Shapiro, G.G., (2003). Effects of ambient air pollution on symptom severity and medication use in children with asthma, Annals of Allergy, Asthma and Immunology, vol. 91, no. 4, (pp. 346–353).
44. Slini, T., Kaprara, A., Karatzas K., Moussiopoulos, N. (2006). PM10 forecasting for Thessaloniki, Greece. Environ Modell Softw., 21:5, (pp. 59–65).
45. Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. and Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling and Software, 22, (pp. 97-103).
46. Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S. (2013). Prediction of 24-hour-average PM2.5concentrations using a hidden Markov model with different emission distributions in Northern California, Science of the Total Environment, 443, (pp. 93–103).
47. Taylor, H., Karlin, S., (1998). An Introduction to Stochastic Modeling. Academic Press, San Diego, California.
48. Tzima, F., Karatzas, K., Mitkas, P., Karathanasis, S. (2007). Using data-mining techniques for PM10 forecasting in the metropolitan area of Thessaloniki, Greece. Proc of the 20th int joint conf on neural networks, Orlando, 275, (pp. 2–7).
49. U.S. EPA (2009). Technical Assistance Document for Reporting of Daily Air Quality-air Quality Index. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina.
50. Voukantsis, D., Karatzas, K., Kukkonen, J., Räsänen, T., Karppinen, A., Kolehmainen, M. (2011). Intercomparison of air quality data using principal component analysis, and forecasting of PM10and PM2.5concentrations using artificial neural networks, in Thessaloniki and Helsinki. Science of the Total Environment, 409, (pp. 1266–1276).
51. Wang, J., Hu, M., Xu, C., Christakos, G., Zhao, Y., (2013). Estimation of citywide air pollution in Beijing. PLOS One 8, e53400.
52. Wang, X., Liu, W. (2012). Research on Air Traffic Control Automatic System Software Reliability Based on Markov Chain, Physics procedia, 24, (pp. 1601 – 1606).
53. WHO Regional Office for Europe, 2006. Air Quality Guideline: Global Updates 2005.
54. Wilks, D.S. (2006). Statistical methods in the atmospheric sciences. Academic Press, USA.
55. Zangooei, H., Delnavaz, M., Asadollahfardi, G.R. (2016). Prediction of coagulation and flocculation processes using ANN models and fuzzy regression, Water Science and Technology. 74 (6), (pp. 1296-1311).
56. Zickus, M., Greig, A.J., Niranjan, M. (2002). Comparison of four machine learning methods for predicting PM10 concentration in Helsinki, Finland. Water Air Soil Pollut, 2:7, (pp. 17–29).
57. Zurada, J.M. (1992). Introduction to Artificial Neural Systems, PWS, Singapore, (pp. 195–196).
58. Asadollahfardi, G.R., Zangooei, H., Aria, S.H., Danesh, E. (2017). Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam, Environmental Quality Management. 26 (3), (pp. 55-72).
59. Barrero, M.A., Grimalt, J.O., Canto'n L. (2006). Prediction of daily ozone concentration maxima in the urban atmosphere. Chemom Intell Lab Syst., 80, (pp. 67–76).
60. Bordignon, S., Gaetan, C., Lisi, F. (2002). Nonlinear models for groundlevel ozone forecasting. Stat Meth Appl., 11:2, (pp. 27–46).
61. Caputo, M., Gimenez, M., Schlamp, M. (2003). Intercomparison of atmospheric dispersion models, Atmos. Environ. 37, (PP. 2435–2449).
62. Chan C.K. and Yao, X. (2008). Air pollution in mega cities in China, Atmospheric Environment, vol. 42, no. 1, (pp. 1–42).
63. Chattopadhay, S. and Chattopadhay, G. (2012). Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis, Pure Appl. Geophys. 169, (pp. 1891–1908).
64. Chen, C., Lin, C.H., Long, Z., Chen, Q. (2014). Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method, Indoor Air, 24, (pp. 81-92).
65. Chen, C., Liu, W., Lin, C.H., Chen, Q. (2015). A Markov chain model for predicting transient particle transport in enclosed environments, Building and Environment, 90, (pp. 30-36).
66. Chung, K.L., AitSahlia, F. (2003), Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance, Springer Undergraduate Texts in Mathematics and Technology, ISSN 0172-6056.
67. Cohen, S., Intrator, N. (2002). Automatic model selection in a hybrid perceptron/ radial network, Information Fusion: Special Issue on Multiple Experts, 3(4), (pp. 259–266).
68. Delnavaz, M., Zangooei, H., Zangooei, M. (2016). Application of Mathematical Models and Fuzzy Regression Analysis to Determine the Microbial Growth Kinetic Coefficients and Predicting Quality of Treated Wastewater, Nature Environment and Pollution Technology. 15 (1), 43.
69. Deng, X., Zhang, F., Rui W. (2013). PM2.5-induced oxidative stress triggers autophagy in human lung epithelial A549 cells, Toxicology in Vitro, vol. 27, no. 6, (pp. 1762–1770).
70. Du, X., Kong, Q., Ge, W., Zhang, S., Fu, L., (2010). Characterization of personal exposure concentration offine particles for adults and children exposed to high ambient concentrations in Beijing. China J. Environ. Sci. 22, (pp. 1757-1764).
71. Eleuteri, A., Tagliaferri, R., Milano, L. (2005). A novel information geometric approach to variable selection in MLP networks. Neural Netw, 18(10):130, (pp. 9–18).
72. Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J. (2015). Artificial neural networks forecasting of PM2.5pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, (pp. 118-128).
73. Goss, C.H., Newsom, S.A., Schildcrout, J.S., Sheppard, L. and Kaufman, J.D. (2004). Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis, American Journal of Respiratory and Critical Care Medicine, vol. 169, no. 7, (pp. 816–821).
74. Hanna, S.R., Paine, R., Heinold, D., Kintigh, E., Baker, D. (2007). Uncertainties in air toxics calculated by the dispersion models AERMOD and ISCST 3 in the Houston ship channel area, J. Appl. Meteorol. Climatol. 46, (pp.1372–1382).
75. Harsham, D.K., Bennett, M. (2008). A sensitivity study of validation of three regulatory dispersion models, Am. J. Environ. Sci. 4, (pp. 63–76).
76. Jones RM, (2014). Nicas M. Benchmarking of a Markov multizone model of contaminant transport, Ann Occup Hyg, 58:10, (pp. 18-31).
77. Kalapanidas, E., Avouris, N. (2001). Short-term air quality prediction using a case-based classifier, Environ Modell Softw.,16:2, (pp. 63–72).
78. Karatzas, K., Kaltsatos, S. (2007). Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul Model Pract Theory, 15(10):131, (pp. 0–9).
79. Kohavi, R., John, G.H. (1997). Wrappers for feature subset selection. Artif Intell., 97, (pp. 273–324).
80. Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M. (2003). 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, Atmos Environ., 37(32), (pp. 39–50).
81. Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York, USA.
82. Kurt, A., Gulbagci, B., Karaca F., Alagha, O. (2008). An online air pollution forecasting system using neural networks, Environment International, 34, (pp. 592–598).
83. Logofet, D.O., Lensnaya, E.V. (2000). The mathematics of Markov models: what Markov chains can really predict in forest successions. Ecol Modell., 2:3, (pp. 285–98).
84. Moustris, K.P., Ziomas I.C. and Paliatsos A.G. (2010). 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 using a neural Networks in Athens, Greece, Water Air Soil Pollut, 209, (pp. 29–43).
85. Moustris, K.P. Larissi, I.K. Nastos P.T. Koukouletsos K.V. and Paliatsos, A.G. (2013). Development and application of artificial neural network modeling in forecasting PM10 Levels in a Mediterranean City, Water air soil pollut. 224: 1634, (pp. 3-11).
86. Nagendra, S.M., Khare, M. (2006). Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions, Ecol Modell, 190, (pp. 99-115).
87. Nicas M. (2000). Markov modeling of contaminant concentrations in indoor air, AIHAJ 61:48.
88. Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J. (2005). Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations, Atmos Environ., 39:65, (pp. 24–36).
89. Niska, H., Heikkinen, M., Kolehmainen, M. (2006). Genetic algorithms and sensivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series, Lect Notes Comput Sci., 4224:2, (pp. 24–31).
90. Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E., and Fila, M. (2006). Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques. Chemometrics and intelligent laboratory systems, 83(1), (pp. 26-33).
91. Qiu, H., Yu, I., Wang, X., Tian, L., Tse, L.A., Wong, T.W., (2013). Differential effects offine and coarse particles on daily emergency cardiovascular hospitalizations in Hong Kong. Atmos. Environ., 64, (pp. 296-302).
92. Rumelhart; D.E., Clelland; J.L.M. (1986). Parallel distribution processing: Exploration in the microstructure of cognition, Cambridge, MA: MIT Press (p. 1).
93. Shamshad, A., Bawadi, M.A., Wan Hussin, W.M.A., Majid, T.A., Sanusi, S.A.M. (2005). First and second order Markov chain models for synthetic generation of wind speed time series, Energy, 30, (pp. 693–708).
94. Slaughter, J.C., Lumley, T., Sheppard, L., Koenig, J.Q., and Shapiro, G.G., (2003). Effects of ambient air pollution on symptom severity and medication use in children with asthma, Annals of Allergy, Asthma and Immunology, vol. 91, no. 4, (pp. 346–353).
95. Slini, T., Kaprara, A., Karatzas K., Moussiopoulos, N. (2006). PM10 forecasting for Thessaloniki, Greece. Environ Modell Softw., 21:5, (pp. 59–65).
96. Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. and Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling and Software, 22, (pp. 97-103).
97. Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S. (2013). Prediction of 24-hour-average PM2.5concentrations using a hidden Markov model with different emission distributions in Northern California, Science of the Total Environment, 443, (pp. 93–103).
98. Taylor, H., Karlin, S., (1998). An Introduction to Stochastic Modeling. Academic Press, San Diego, California.
99. Tzima, F., Karatzas, K., Mitkas, P., Karathanasis, S. (2007). Using data-mining techniques for PM10 forecasting in the metropolitan area of Thessaloniki, Greece. Proc of the 20th int joint conf on neural networks, Orlando, 275, (pp. 2–7).
100. U.S. EPA (2009). Technical Assistance Document for Reporting of Daily Air Quality-air Quality Index. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina.
101. Voukantsis, D., Karatzas, K., Kukkonen, J., Räsänen, T., Karppinen, A., Kolehmainen, M. (2011). Intercomparison of air quality data using principal component analysis, and forecasting of PM10and PM2.5concentrations using artificial neural networks, in Thessaloniki and Helsinki. Science of the Total Environment, 409, (pp. 1266–1276).
102. Wang, J., Hu, M., Xu, C., Christakos, G., Zhao, Y., (2013). Estimation of citywide air pollution in Beijing. PLOS One 8, e53400.
103. Wang, X., Liu, W. (2012). Research on Air Traffic Control Automatic System Software Reliability Based on Markov Chain, Physics procedia, 24, (pp. 1601 – 1606).
104. WHO Regional Office for Europe, 2006. Air Quality Guideline: Global Updates 2005.
105. Wilks, D.S. (2006). Statistical methods in the atmospheric sciences. Academic Press, USA.
106. Zangooei, H., Delnavaz, M., Asadollahfardi, G.R. (2016). Prediction of coagulation and flocculation processes using ANN models and fuzzy regression, Water Science and Technology. 74 (6), (pp. 1296-1311).
107. Zickus, M., Greig, A.J., Niranjan, M. (2002). Comparison of four machine learning methods for predicting PM10 concentration in Helsinki, Finland. Water Air Soil Pollut, 2:7, (pp. 17–29).
108. Zurada, J.M. (1992). Introduction to Artificial Neural Systems, PWS, Singapore, (pp. 195–196).

بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.