ارزیابی عملکرد روش‌های شبکه‌ عصبی مصنوعی و سیستم استنتاج عصبی-فازی تطبیقی در تخمین تبخیر-تعرق گیاه مرجع در شرایط گلخانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 استاد گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج

3 گروه علوم باغبانی ، دانشکده مهندسی و فناوری کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

چکیده

برآورد دقیق تبخیر-تعرق گیاه مرجع (ETo) یکی از عوامل مهم در مدیریت و برنامه‌ریزی آبیاری در بخش کشاورزی است. استفاده از روش استاندارد فائو-پنمن-مانتیت برای تخمین ETo، مشروط به در دسترس بودن پارامترهای اقلیمی شامل دما، رطوبت، تابش، سرعت باد و هم-چنین فراهم بودن فرضیات ذکر شده در نشریه فائو 56 است. در بعضی مناطق یا در کشت‌های کنترل شده گلخانه، اغلب دسترسی به تمام پارامترهای اقلیمی و یا برآوردن فرضیات روش فائو-پنمن-مانتیت امکان‌پذیر نیست. بنابراین، بهره‌گیری از روش‌هایی که با پارامترهای کمتری بتواند تخمین دقیقی از ETo ارائه نماید، ارجح است. پژوهش حاضر با هدف ارزیابی عملکرد دو روش شبکه‌های عصبی مصنوعی (ANNs) و سیستم استنتاج عصبی-فازی تطبیقی (ANFIS) برای برآورد ETo در گلخانه تحقیقاتی واقع در دانشکده کشاورزی و منابع طبیعی دانشگاه تهران انجام شد. بر اساس پارامترهای اقلیمی اندازه‌گیری شده در داخل گلخانه، ترکیب‌های مختلفی ایجاد و شاخص‌های ارزیابی برای هر روش و سناریو محاسبه گردیدند. بهترین ساختار شبکه عصبی برای سناریو 4 (ورودی‌های تابش، دما، رطوبت) با 7 نورون در لایه میانی و الگوریتم آموزش تنظیم بیزی به دست آمد. طراحی شبکه‌های ANFIS با توابع عضویت‌های مختلف انجام شد. نتایج نشان داد، تفاوت قابل ملاحظه‌ای بین دقت مدل‌سازی ETo در روش ANFIS تحت سناریوهای مختلف وجود ندارد. به عبارت دیگر در این روش، حتی با داده‌های دما و رطوبت نیز می‌توان با دقت بالایی ETo در داخل گلخانه را شبیه‌سازی نمود. مقایسه شاخص‌های ارزیابی بین مدل‌های ANFIS و ANNs نشان داد، مدل ANFIS عملکرد بهتری نسبت به روش ANNs دارد. به طوری‌که شاخص جذر میانگین مربعات خطای نسبی (RRMSE)، برای سناریوهای 1 تا 4 در مدل ANFIS به ترتیب برابر با 41/1، 80/0، 06/1 و 01/1 درصد و در مدل ANNs برابر با 70/12، 23/2، 12/2 و 10/2 درصد بود.

کلیدواژه‌ها


عنوان مقاله [English]

Performance Evaluation of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System in Estimating Reference Evapotranspiration under Greenhouse Conditions

نویسندگان [English]

  • Hadisseh Rahimikhoob 1
  • Teymour Sohrabi 2
  • Mojtaba Delshad 3
1 Irrigation and Reclamation Engineering Department, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 professor of Irrigation and Development Engineering Group, Faculty of Agriculture Engineering & Technology, Agriculture and Natural Resources Campus, University of Tehran,karaj
3 Horticultural Sciences Department, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

One of the important factors for agricultural irrigation management and planning is accurate estimation of reference evapotranspiration (ETo). The application of the standard model (FAO-Penman-Monteith) to estimate ETo is restricted due to the availability of climatic variables including temperature, humidity, radiation, wind speed as well as the availability of the mentioned hypotheses in FAO 56. Accessibility to all climatic parameters or satisfaction of the FAO-Penman-Monteith assumptions are often not possible in some areas or in controlled environments (greenhouses). Therefore, it is preferable to use methods that can provide an accurate estimate of ETo with fewer input parameters. The aim of the present study was to evaluate the performance of two methods of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) for estimating ETo in the research greenhouse of the College of Agriculture and Natural Resources of the University of Tehran, located in Karaj, Iran. Based on the measured climatic parameters inside the greenhouse, different combinations were created and evaluation indicators were calculated for each method and scenario. The best neural network structure was obtained for Scenario 4 (radiation, temperature, humidity) with 7 neurons in the hidden layer and Bayesian Regularization training algorithm. ANFIS model was designed with different membership functions. The results showed that there was no significant difference between the performance of the ANFIS method under different scenarios. In other words, even with temperature and humidity data, ETo can be simulated with high accuracy by ANFIS method. Comparison of evaluation indicators between ANFIS and ANNs models showed that ANFIS performed better than ANNs method. The calculated relative root mean square error (RRMSE) for scenarios 1 to 4 in ANNs model was equal to 12.70, 2.23, 2.12 and 2.10% however it was equal to 1.41, 0.80, 1.06 and 1.01% in ANFIS model.

کلیدواژه‌ها [English]

  • Reference evapotranspiration
  • Artificial neural networks
  • Adaptive neuro-fuzzy inference system
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دوره 16، شماره 3 - شماره پیاپی 93
مرداد و شهریور 1401
صفحه 499-511
  • تاریخ دریافت: 19 بهمن 1400
  • تاریخ بازنگری: 16 اسفند 1400
  • تاریخ پذیرش: 11 اردیبهشت 1401
  • تاریخ اولین انتشار: 11 اردیبهشت 1401