Evaluation of Artificial Neural Network for determining distribution pattern of ascid family (Acari: Mesostigmata) in Damghan

In this study, the artificial neural network methods were used to estimate the distribution of ascid family (Acari: Mesostigmata). For this aim, latitude, longitude and elevation from the sea level of 137 points were defined as inputs and output of method was number of species of this family on thos...

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Bibliographic Details
Published in:	Journal of Entomological Society of Iran : J.E.S.I Vol. 37; no. 3; pp. 361 - 368
Main Authors:	M. Hakimitabar, A. R. Shabaninejad, A. Saboori, M. H. Shams
Format:	Journal Article
Language:	English
Published:	Entomological Society of Iran 01-11-2017
Subjects:	distribution patterns perceptron predatory mites propagation algorithm
Online Access:	Get full text
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Summary:	In this study, the artificial neural network methods were used to estimate the distribution of ascid family (Acari: Mesostigmata). For this aim, latitude, longitude and elevation from the sea level of 137 points were defined as inputs and output of method was number of species of this family on those points and Perceptron with propagation algorithm was evaluated in artificial neural network method. To evaluate the ability of neural networks used to predict dispersion, statistical comparison of parameters such as variance, statistical distribution and mean of spatial predicted values by neural network and their actual values were used. The results showed that there was no significant difference (p> 0.4) in the training and test phases between the values of the statistical characteristics of variance, the statistical distribution and the mean of real and predicted spatial data of this family by the neural network. It can be concluded that the artificial neural network method was able to predict the dispersion of this family with proper precision by integrating three factors of latitude and longitude and elevation from the sea level. ;font-family:"Times New Roman","serif"; mso-bidi-font-family:"B Lotus";mso-bidi-language:FA'>p> 0.4). در مجموع میتوان چنین نتیجه گرفت که روش شبکه عصبی مصنوعی با تلفیق سه عامل طول و عرض جغرافیایی و ارتفاع از سطح دریا، قادر به پیشبینی پراکندگی این خانواده با دقت مناسب بود.
ISSN:	0259-9996 2783-3968
DOI:	10.22117/jesi.2017.116045.1149