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
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