پیش بینی عملکرد و کارآیی مصرف آب زعفران با استفاده از مدل های شبکه عصبی مصنوعی بر مبنای فاکتورهای اقلیمی و آب

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه آبیاری، دانشکده کشاورزی، دانشگاه زنجان، ایران

2 استادیار گروه کشاورزی، دانشگاه پیام نور، ایران

چکیده

با پدید آمدن تکنیک‌های آماری قوی و شبکه‌های عصبی، مدل‌های پیش‌بینی کننده عملکرد محصولات زراعی به‌سرعت رو به توسعه است. بدین منظور آزمایشی در منطقه تربت‌حیدریه با هدف پیش‌بینی عملکرد و کارآیی مصرف آب زعفران با استفاده از مدل شبکه عصبی مصنوعی انجام گرفت. واسنجی و اعتباریابی مدل‌ها نیز با استفاده از آمار عملکرد محصول و پارامترهای اقلیمی سال 91-1390 صورت پذیرفت. ارزیابی مدل‌ها نیز با شاخص‌های آماری ضریب تبیین (R2)، جذر میانگین مربعات خطا نرمال شده (RMSEn) و میانگین مربعات خطا (MSE) انجام شد. نتایج تحقیق نشان داد که شبکه عصبی پیشنهادی (مدل شماره 9) با داشتن 2 لایه پنهان، 8 نورون و ضریب تبیین 97/0 برای عملکرد و 1 لایه پنهان، 7 نورون و ضریب تبیین 90/0 برای کارآیی مصرف آب، برازش خوبی برای این دو صفت داشت. همچنین مطابق با شاخص‌های آماری RMSEn و MSE در مدل پیشنهادی (مدل شماره 9) که به ترتیب برابر بود با 78/2 درصد و 0040/0 برای عملکرد و 41/5 درصد و 0073/0 برای کارآیی مصرف آب، بالاترین دقت برای پیش‌بینی صفات فوق مشاهده شد. تحلیل حساسیت مدل‌ها نیز نشان داد که عملکرد و کارآیی مصرف آب محصول زعفران، بیشترین حساسیت را به عامل آبیاری، سپس بارندگی و درنهایت ساعات آفتابی دارد. به‌طورکلی، کاربرد شبکه عصبی پیشنهادی در این تحقیق می‌تواند زمینه ارتقاء محصول زعفران را در منطقه تربت فراهم نماید.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting Yield and Water Use Efficiency in Saffron Using Models of Artificial Neural Network Based on Climate Factors and Water

نویسندگان [English]

  • Azim Shirdeli 1
  • Abolfazl Tavassoli 2
1 Assistant Prof. Department of Agriculture, Zanjan University, Iran
2 Assistant Prof. Department of Agriculture, Payame Noor University, I. R. of Iran
چکیده [English]

The predicted models for crops yield are developing rapidly by the creation of new statistical techniques and neural networks. For this purpose, a research was carried out in the Torbat-e-Heydarieh region for predicting yield and water use efficiency of saffron by using an artificial neural network model. The model was calibrated and validated by using crop yield and climate parameters data during 2009-2010. The models were evaluated by using indices of correlation coefficient (R2), root mean squares error normalized (RMSEn), and mean squares error (MSE). The results showed that the suggested neural network (model No. 9) with having 2 hidden layers, 8 neurons, and R2= 0.97 (for saffron yield); and 1 hidden layer, 7 neurons, and R2= 0.90 (for water use efficiency) had a high accommodation with these two factors. Also, according to the indices RMSEn and MSE, model No. 9 simulated the yield and WUE of saffron with a high accuracy, such that RMSEn and MSE for yield in this model obtained were 2.78% and 0.0041, respectively; and for WUE they were calculated to be 5.41% and 0.0073, respectively. Also, the results of sensitivity analysis indicated that irrigation is the most important parameter for predicting yield and WUE, and after that is precipitation and solar radiation. Generally, use of the suggested neural network in this research can improve saffron cultivation in the Torbat-e-Heydarieh region.

کلیدواژه‌ها [English]

  • Model
  • Simulation
  • Stigma
  • Torbat-e-Heydarieh

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