Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mohamadi Ronak; Shabaninejad Alireza; ALICHI MAHMOOD; Shabani Nejad Mohammad Reza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Entomological Society of Iran Year:2018 | Volume:38 | Issue:3 (75) Page(s): 275-287

Download Full-Text

Persian Verion

View:

262

Download:

Cites:

Information Journal Paper

Title

Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz

Author(s)

Mohamadi Ronak | Shabaninejad Alireza | ALICHI MAHMOOD | Shabani Nejad Mohammad Reza | Issue Writer Certificate

Keywords

Coccinella septempunctata LQ1

GMDH artificial neural networkQ1

spatial distributionQ1

Abstract

This study aimed to predict population of Coccinella septempunctata in the field using artificial neural network. The data was collected from a four-hectare field in years of 2013-2014 in the area of Badjga Shiraz. In this model, the input variables were, longitude and latitude and population changes of Coccinella septempunctata was used as the outcome variable. The neural network type used, was Group Method of Data Handling (GMDH) that optimized by genetic algotithm. To evaluate the ability of GMDH neural networks to predict the spatial distribution of the species, statistical comparison of the parameters such as mean, variance, statistical distribution and coefficient determination of linear regression between predicted values and actual values was used. Results showed that in training and test phases of GMDH, there was no significant effect between variance, mean and statistical distribution of actual and predicted values, and the coefficient of determination of 0. 98 indicates the high accuracy of this neural network in predicting the density of this species. The drawn maps showed that the distribution of this natural enemy is patchy.

Cites

No record.

References

No record.

Cite

APA: Copy

Mohamadi, Ronak, Shabaninejad, Alireza, ALICHI, MAHMOOD, & Shabani Nejad, Mohammad Reza. (2018). Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz. JOURNAL OF ENTOMOLOGICAL SOCIETY OF IRAN (JESI), 38(3 (75) ), 275-287. SID. https://sid.ir/paper/399973/en

Vancouver: Copy

Mohamadi Ronak, Shabaninejad Alireza, ALICHI MAHMOOD, Shabani Nejad Mohammad Reza. Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz. JOURNAL OF ENTOMOLOGICAL SOCIETY OF IRAN (JESI)[Internet]. 2018;38(3 (75) ):275-287. Available from: https://sid.ir/paper/399973/en

IEEE: Copy

Ronak Mohamadi, Alireza Shabaninejad, MAHMOOD ALICHI, and Mohammad Reza Shabani Nejad, “Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz,” JOURNAL OF ENTOMOLOGICAL SOCIETY OF IRAN (JESI), vol. 38, no. 3 (75) , pp. 275–287, 2018, [Online]. Available: https://sid.ir/paper/399973/en

Related Journal Papers