کاربرد روش های فراکاوشی در تخمین عملکرد گندم

نوع مقاله: مقاله پژوهشی

نویسندگان

1 عضو هیئت علمی

2 -

3 عضو هیئت علمی گروه علوم و مهندسی خاک دانشگاه زنجان

چکیده

افزایش تقاضای محصولات کشاورزی و کمبود منابع آب و خاک مناسب همراه با مشکلات تحقیقات میدانی، ضرورت استفاده از مدل‌های مناسب برای پیش‌بینی عملکرد محصولات کشاورزی را آشکار می‌سازد. این تحقیق به بررسی کارایی مدل‌های فراکاوشی شبکه‌های عصبی مصنوعی، شبکه تطبیقی عصبی فازی و روش ترکیبی شبکه‌های عصبی- فازی تطبیقی و الگوریتم بهینه‌سازی ازدحام ذرات دربرآورد عملکرد گندم به کمک ویژگی‌های خاک و اراضی پرداخته ‌است. منطقه مورد مطالعه در شهرستان هریس (استان آذربایجان شرقی) قرار داشته و رژیم حرارتی و رطوبتی به ترتیب مزیک و اریدیک هم مرز با زریک می‌باشد. جهت نیل به اهداف در تحقیق حاضر 80 خاکرخ در مزارع گندم منطقه حفر گردید. از هر افق ژنتیکی یک نمونه خاک برداشت و به آزمایشگاه منتقل و تجزیه‌های فیزیکی و شیمیایی بر روی آن‌ها انجام شد. نتایج آنالیز حساسیت مدل نشان داد که نیتروژن کل، فسفر قابل جذب، درصد شیب، درصد سنگریزه، واکنش خاک و ماده آلی به عنوان ویژگی‌های تاثیرگذار اراضی در عملکرد گندم هستند. کارایی مدل‌های مذکور با موفقیت برای تشریح رابطه بین عملکرد گندم و ویژگی-های زودیافت بررسی شد. مدل ترکیبی نروفازی-ازدحام ذرات که یک روش ترکیبی عصبی، فازی و ازدحام ذرات بوده از نظر آماره‌های ضریب تبیین (89/0) و جذر میانگین مربعات خطا (5/213) عملکرد بهتری نسبت به دو مدل شبکه‌های عصبی مصنوعی و نروفازی دارد. همچنین، روش نروفازی دارای ضریب تبیین (84/0) و جذر میانگین انحراف مربعات خطا (2/243) و شبکه‌های عصبی مصنوعی دارای ضریب تبیین (81/0) و جذر میانگین نحراف مربعات (5/274) بود. معیار میانگین هندسی نسبت خطا (GMER) نیز نشان‌دهنده بیش‌برآوردی مدل شبکه‌ عصبی مصنوعی (24/0) و نروفازی (53/0) و کم-برآوردی مدل نرفازی-ازدحام (13/1) می‌باشد. نتایج نشان می‌دهد که مدل ترکیبی نروفازی–ازدحام ذرات به‌عنوان مدل کارا می‌تواند به‌عنوان یک ابزار قدرتمند در تخمین عملکرد گندم باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Application of Heuristic Methods in Prediction of Wheat Yield

نویسندگان [English]

  • Moslem Servati 1
  • Ali Barikloo 2
  • Parisa Alamdari 3
  • Kamran Moravej 3
2 -
3 -
چکیده [English]

Increasing demand for agricultural products and lack of appropriate soil and water resources with problems of field research reveals the application of efficient models to predict crop yield. This research aimed to examines the efficiency of artificial neural networks, comparative fuzzy neural network, adaptive nero fuzzy inference system and particle swarm optimization algorithm models for estimating the wheat yield through soil and land properties. For this purpose, 80 soil profiles were drilled in wheat fields’area in East Azerbaijan province with temperature and moisture regimes of mesic and aridic border to xeric, respectively. Soil samples were collected from each genetic horizon. The results of sensitivity analysis showed that total nitrogen, absorbable phosphorus, slope, gravel, soil reaction and organic matter are effective soil properties in wheat yields. The hybrid model of PSO-ANFIS was the best model from the viewpoint of statistical indices including R2 (0.89) and RMSE (213.5). Also, neuro-fuzzy method has a R2 (0.84) and RMSD (243.2) and artificial neural networks have a R2 (0.81) and RMSD (274.5), respectively. The GMER index also indicated overestimation of artificial neural network (0.24) and nero fuzzy (0.53) and underestimation of PSO-ANFIS model (1.13). The results indicated that the hybrid neuro-fuzzy-swarm particles model performed better than other models that can be used a powerful tool for estimating wheat yield.

کلیدواژه‌ها [English]

  • Artificial Neural Networke
  • Adaptive Nero fuzzy inference system
  • Particle Swarm Optimization
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