ترکیب روش زمین‌ آمار با شبکه عصبی مصنوعی بهینه شده با الگوریتم ژنتیک در پیش‌بینی الگوی توزیع کفشدوزک هفت‌نقطه‌ایCoccinella septempunctata در مزرعه یونجه شهرستان باجگاه

نوع مقاله : مقاله کامل، فارسی

نویسندگان

1 گروه آموزشی گیاهپزشکی، دانشکده کشاورزی، دانشگاه رازی کرمانشاه

2 دانشجوی دکترا گرایش حشره شناسی، دانشکده کشاورزی، دانشگاه رازی کرمانشاه

3 گروه آموزشی گیاهپزشکی، دانشکده کشاورزی دانشگاه شیراز

چکیده

با پدید آمدن تکنیک‌های آماری قوی و شبکه‌های عصبی، مدل‌های پیش بینی کننده پراکنش موجودات به سرعت در اکولوژی توسعه پیدا کرده است. با توجه به دشواری نمونه برداری معمولا در این گونه مطالعات تعداد نمونه کافی وجود ندارد لذا برای رفع این مشکل در این پژوهش به منظور پیش بینی و ترسیم نقشه توزیع کفشدوزک هفت‌نقطه‌ایاز ترکیب روش کریجینگ با شبکه‌های عصبی پرسپترون چندلایه (MLP) ترکیب شده با الگوریتم ژنتیک در سطح مزرعه استفاده شد. داده‌های مربوط به جمعیت این آفت از طریق نمونه برداری از سطح یک مزرعه در شهرستان باجگاه در سال 13۹۲ بدست آمده. داده­ها توسط روش کریجینگ معمولی با نیم تغییرنمای کروی که بهترین عملکرد را داشت میانیابی شدند و به عنوان ورودی شبکه عصبی معرفی شدند. برای ارزیابی قابلیت شبکه‌های عصبی مورد استفاده در پیش بینی توزیع از مقایسه آماری پارامترهایی مانند واریانس، توزیع آماری بین مقادیر پیش بینی شده مکانی توسط شبکه عصبی و مقادیر واقعی آن‌ها استفاده شد. نتایج نشان داد که در فازهای آموزش و آزمایش بین مقادیر ویژگی‌های آماری واریانس و توزیع آماری مجموعه داده‌های واقعی و پیش بینی شده مکانی این آفت توسط شبکه عصبی ترکیب شده، تفاوت معنی­داری وجود نداشت. نقشه‌های ترسیم شده نشان داد که توزیع آفت تجمعی است.

کلیدواژه‌ها

عنوان مقاله [English]

An Application of Combined Geostatistics with Optimized Artificial Neural Network by Genetic Algorithm to estimate the distribution of Coccinella septempunctata (Col:. Coccinellidae) in the alfalfa farm of Bajgah

نویسندگان [English]

  • R. Mohamadddi 1
  • Alireza Shabani 2
  • M. Alichi 3
1 department of plant protoction, college of agricalture, Razi university
2 Phd student department of plant protoction, college of agricalture, Razi university
3 Department of plant protoction, college of agricalture, Shiraz uviversity
چکیده [English]

Today, with the advance statistical techniques and neural networks, predictive models of distribution were rapidly developed in ecology. Due to the difficulty of sampling, there are usually not enough samples in such studies. Therefore, in order to predict and mapping the distribution of Coccinella
septempunctata used the combination of the Kriging method with multilevel perceptron neural
networks (MLP) combined with genetic algorithm at the farm level. Population data of pest was
obtained in 2014 by sampling in 221 fixed points in the alfalfa farm of Bajgah. The data was interpolated by ordinary Kriging method with spherical variogram, which had the best performance, and introduced as a neural network input. To evaluate the ability combined geostatistics with optimized artificial neural network by genetic to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and between predicted values and actual values. Results indicating that there was non-significant difference between statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated Coccinella septempunctata density. Our map showed that pest distribution was patchy.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Genetic algorithm
  • spatial distribution
  • Coccinella septempunctata

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