ارایه تکنیک پیش بینی غیر- نظارت شونده در برآورد تبخیر-تعرق گیاه مرجع

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آبیاری و زهکشی/ دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران

2 دانشجوی کارشناسی ارشد آبیاری و زهکشی/دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران

3 استاد /گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، ایران

4 دانشیار هیدرولوژی، دانشکده منابع طبیعی، گروه احیای مناطق خشک و کوهستانی، دانشگاه تهران، ایران

چکیده

تبخیر-تعرق از اجزاء اصلی چرخه هیدرولوژی است و در تعیین نیاز آبی گیاه، مطالعات بیلان آبی و مدیریت منابع آب نقش مهمی دارد. تاکنون روش‌های مستقیم و غیر مستقیم متعددی برای برآورد تبخیر- تعرق گیاه مرجع ارائه شده است، اما هر یک از این روش‌ها دارای محدودیت‌هایی هستند. به عنوان مثال، از محدودیت‌های روش‌های اندازه‌گیری مستقیم می‌توان به عدم دقت وسایل اندازه‌گیری و مسائل مربوط به مقیاس اشاره کرد، در حالیکه روش‌های غیر مستقیم نظیر معادله پنمن-مانتیث، به پارامترهای اقلیمی روزانه زیادی نیاز دارند. در این تحقیق سعی گردید از روش نگاشت خود-سامان به عنوان یک روش شبکه عصبی مصنوعی غیر نظارت شونده در پیش‌بینی تبخیر-تعرق با حداقل پارامترهای هواشناسی به عنوان ورودی، استفاده گردد. براساس شاخص‌ های ارزیابی خوشه‌ بندی فازی، مقادیر ETo در مشهد به دو خوشه با تبخیر-تعرق کم و زیاد  تقسیم شد که با اقلیم منطقه مطابقت نشان داد. همچنین به منظور ارزیابی کارایی مدل ارائه شده از معیارهای آماری شامل (ریشه میانگین مربعات خطا، ضریب تعیین ومعیار ناش-ساتکلیف) استفاده گردید و نتایج حاصله با برآوردهای حاصل از مدل‌های تجربی مقایسه گردید. نتایج حاصله نشان داد که حتی ساده‌ترین مدل نگاشت خود-سامان با ترکیب متوسط دمای هوا و حداکثر ساعات آفتابی به عنوان ورودی نیز خطای کمتری نسبت به معادلات تجربی دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Using Unsupervised Estimator Technique to Predict Reference Crop Evapotranspiration

نویسندگان [English]

  • F. Farsadnia 1
  • S. Zahmati 2
  • B. Ghahreman 3
  • A.R. Moghaddam Nia 4
1 Ph.D. Student in irrigation and drainage, Department of Water EngineeringCollege of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
2 M.Sc. Student, Department of Water Engineering, College of Agriculture
3 Professor, Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
4 Associate Professor of Hydrology, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Evapotranspiration is the main component of hydrologic cycle and has an important role in crop water requirement estimations, water balances studies, and water resource management. There are a lot of direct and indirect methods to estimate reference crop evapotranspiration, but each has some limitations. For example, limitations that can be mentioned for direct measuring are the insufficient precision in measuring devices and the scale problems. An indirect method like Penman-Monteith on the other hand needs a lot of daily climatic parameters. This research tried to use self-organizing maps as an unsupervised artificial neural network method to predict evapotranspiration by minimum meteorological data input. Based on fuzzy clustering indices, evapotranspiration values in the study area, Mashhad plain, are divided into two clusters with low and high ETo coincided with the climate of the area. Also, in order to validate the model, statistical indices containing root mean square error, determination coefficient, and Nash–Sutcliffe model efficiency coefficient are used and the results are compared with the experimental models output. The results showed that even the simplest SOM model which employs mean temperature and maximum sunshine duration as input have less errors compared to the experimental equations.

کلیدواژه‌ها [English]

  • Self-Organizing map
  • FAO-Penman-Monteith equation
  • Crop reference evapotranspiration
  • Mashhad plain
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