تخمین بیشینه، متوسط و کمینه دمای هوای شهر تبریز با استفاده از روشهای هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب دانشگاه تبریز

چکیده

تخمین دمای هوای هر منطقه یکی از مسائل مهم در برنامهریزی کشاورزی و نیز مدیریت منابع آب میباشد که به
روشهای مختلفی همچون مدلهای تجربی، نیمه تجربی و هوشمند قابل انجام است. در تحقیق حاضر از سیستم استنتاج
عصبی– فازی تطبیقی، شبکههای عصبی مصنوعی و برنامهریزی ژنتیک برای تخمین مقادیر دمای هوا در ایستگاه
سینوپتیک شهر تبریز، واقع در شمال غرب ایران استفاده شده است. با توجه به شاخصهای آماری، هر سه مدل با دقت
قابل قبولی قادر به تخمین دقیق مقادیر دمای کمینه، متوسط و بیشینه هوا میباشند و با وجود تفاوت جزیی در دقت
تخمین و خطای مدلها، سیستم استنتاج عصبی – فازی تطبیقی، شبکههای عصبی مصنوعی و برنامهریزی ژنتیک به-
ترتیب در اولویتهای اول تا سوم قرار میگیرند. همچنین راه حلهای صریحی که نشانگر ارتباط بین متغیرهای ورودی
و خروجی باشد ، بر مبنای برنامهریزی ژنتیک ارائه گردیده است که ارجحیت برنامهریزی ژنتیک بر دو مدل دیگر را در
این زمینه میرساند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimation of Maximum, Mean and Minimum Air Temperature in Tabriz City Using Artificial Intelligent Methods

نویسندگان [English]

  • MA Ghorbani
  • J Shiri
  • H Kazemi
چکیده [English]

Estimating air temperature is one of the important issues in agricultural planning and in water
resources management which can be accomplished by using different methods such as empirical,
semi-empirical and intelligent methods. In the present study, Adaptive Neuro Fuzzy Inference
System, Artificial Neural Networks and Genetic Programming were used to estimate air
temperature in the synoptic station of Tabriz City, northwest of Iran. Considering the statistical
indices, all three models were able to estimate accurately minimum, mean and maximum air
temperature. In spite of slight differences in the prediction accuracy and errors by the models,
Adaptive Neuro Fuzzy Inference System, Artificial Neural Networks and Genetic Programming
were in the order of priority. Also explicit solutions that show the relation between input and output
variables are presented based on Genetic Programming. This adds to the superiority of Genetic
Programming over the other two models.

کلیدواژه‌ها [English]

  • Adaptive neuro fuzzy inference system
  • Artificial Neural Networks
  • genetic programming
  • Tabriz air temperature
عزیزی ق و روشن م؛ 1387 . مطالعه تغییر اقلیم در سواحل جنوبی دریای خزر به روش من – کندال؛ فصلنامه پژوهش-
. های جغرافیایی، شماره 64 ، صفحات 13 تا 28
قویدل رحیمی ی؛ 1385 . ارزیابی حساسیت پذیری دما و بارش تبریز به افزایشکربن دی اکسید جو با استفاده از
، مدلهای گردش جهانی پیوندی جوی – اقیانوسی؛ فصلنامه مدرس علوم انسانی، ویژه نامه جغرافیا، شماره 48
. صفحات 103 تا 123
Abde-Al ME and Elhadidi MA, 1994. A machine learning approach to modeling and forecasting
the minimum temperature at Hdahran, Saudi Arabia Energy 7:739-749.
Allen CC, 1957. A simplified equation for minimum temperature prediction. Monthly Weather
Review 85, pp. 119-120.
Aytek A and Kisi O, 2008. A genetic programming approach to suspended sediment modeling.
Journal of Hydrology 351:288-298.
Bagdonas A, Georg JC and Gerber JF, 1978. Techniques of frost prediction and methods of frost
and cold protection. WMO, Tech note No 157. Genevra.
Banzhaf, W, Nordin P, Keller RE and Francone FD, 1998. Genetic Programming. Morgan
Kaufmann, San Francisco, CA.
Benavides R, Montes F, Rubio A and Osoro K, 2007. Geostatistical modeling of air temperature in
a mountainous region of northern Spain. Agric and Forest Meteorol 146:173-188.
Brunt D, 1941. Physical and Dynamical Meteorology. 2nd edition, Cambridge University Press,
New York.
Dadson R and Marks D, 1997. Daily air temperature interpolated at high spatial resolution over a
large mountainous region. Climatic Res 8:1-20.
Dombayc, ÖA and Gölcü M, 2009. Daily means ambient temperature prediction using artificial
neural network method: A case study of Turkey. Renewable Energy 34:1158-1161.
Drake JT, 2000. Communications phase synchronization using the adaptive network fuzzy
inference system. Ph.D. Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
تخمین بیشینه، متوسط و کمینه دمای هوای شهر تبریز با استفاده از ... 103
Elizondo DA, McClendon RW and Hoogenboom G, 1994. Neural network models for predicting
flowering and physiological maturity of soybean. Trans ASABE 37: 981-988.
Ferreira C, 2001. Gene expression programming: a new adaptive algorithm for solving problems.
Complex Syst 13: 87-129.
Figuerola PI and Mazzeo NA, 1997. An analytical model for the prediction of nocturnal and dawn
surface temperatures under calm, clear sky conditions. Agric and Forest Meteorol 85: 229- 237.
Francl LJ and Panigrahi S, 1997. Artificial neural network models of wheat leaf wetness. Agric. and
Forest Meteorol 88: 57-65.
George RK, 2001. Prediction of soil temperature by using artificial neural networks algorithms.
Non linear analysis 47:1737-1748.
Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning.
Addison –Wesley, Reading, Mass.
Haykin S, 1998. Neural Networks: a Comprehensive Foundation. Prentice-Hall, Upper Saddle
River, NJ.
Hudson G and Wackernagel H, 1994. Mapping temperature using kriging with external drift: theory
and example from Scotland. Int J Climatol 14: 77-91.
Jaeger JC, 1945. Note on the effect of wind on nocturnal cooling. Q J R Meteorol Soc 71:388-390.
Jang JSR, 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Manag
Cyber 23: 665-685.
Jang JSR, Sun CT and Mizutani E, 1997. Neurofuzzy and Software Computing: a Computational
Approach to Learning and Machine Intelligence. Prentice-Hall, New Jersey.
Khu ST, Liong SY, Babovic V, Madsen H and Muttil N, 2001. Genetic programming and its
application in real- time runoff forming. Journal of American Water Resources Association
37:439-451.
Kisi O, Ozturk O, 2007. Adaptive neuro-fuzzy computing technique for evapotranspiration
estimation. J Irrig. and Drain Eng ASCE 133: 368-379.
Kozza JR, 1992. Genetic Programming: On the Programming of Computers by Means of Natural
Selection. Cambridge, MA: The MIT Press.
Krasovitski B, Kimmel A and Amir I, 1996. Forecasting air surface temperature for the optimal
application of frost protection methods. J Agric Eng Res 63: 93-102.
Liong SY, Gautam TR, Khu ST, Babovic V, Keijzer M and Muttil N, 2002. Genetic programming:
A new paradigm in rainfall runoff modeling. Journal of American Water Resources Association
38:705-718.
Lippman R, 1987. An introduction to computing with neural nets. IEEE ASSP Mag 4:4-22.
20 شماره 3 / سال 1389 / 104 قربانی، شیری و .... مجله دانش آب و خاک، جلد 1
Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S and Han D, 2009. Evaporation estimation
using artificial neural networks and adaptive neuro-fuzzy inference system techniques.
Advances in Water Resources. 32:88-97.
Paruelo JM and Tomasel F, 1997. Prediction of functional characteristics of ecosystems: a
comparison of artificial neural networks and regression models. Ecological modeling. 98:173-
186.
Patterson DW, 1996. Artificial Neural Networks: Theory and Applications. Simon and Schuster,
Singapore.
Robinson C and Mort N, 1997. A neural network system for the protection of citrus crops from frost
damage. Comput and Electro Agric 16: 177-187.
Rubio A, Sanchez O, Gomez V, Grana D, Elena R and Blanco A, 2002. Auto-ecology of chestnut
tree forest in Castilla, Spain. Investigation Agraria: Sistemas de Recursos Forestales. 11:373-
393.
Smith BA, Hoogenboom G and McClendon RW, 2009. Artificial neural networks for automated
year-round temperature prediction. Comput and Electro Agric 68:52-61.
Soarse J, Oliveria AP, Boznar MZ, Mlakar P, Escobedo JF and Machado AJ, 2003. Modeling
hourly diffuse solar radiation in the city of Sao Paulo using a neural network technique.
Applied Energy 79:201-214.
Ustaoglu B, Cigizoglu HK and Karaca M, 2008. Forecast of daily minimum, maximum and mean
temperature time series by three artificial neural networks. Meteorol Appl 15, 431-445.
Zuzel JF and Cox LM, 1975. Relative importance of meteorological variables in snowmelt. Water
Resour Res 11:174-176.