عنوان مقاله [English]
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.
Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B. & Viau, A. A. (2003) Classification of hyper spectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture 39(12), 67–93.
Hughes, M. A. (1983) The mites of stored food and houses. Technology Bulletin 9, 314–334.
Irmak, A., Jones, J. W., Batchelor, W. D., Irmak, S., Boote, K. J. & Paz, J. (2006) Artificial neural network model as a data analysis tool in precision farming. Transactions of the American Society of Agricultural and Biological Engineers 49, 2027–2037.
Krantz, G. W. & Lindquist, E. E. (1979) Evolution of phytophagous mites (Acari). Annual Review of Entomology 24, 121–158.
Krantz, G.W. & Walter, D.E. (eds.) (2009) A Manual of Acarology. 3rd ed. 807 pp. Texas Technology University Press.
Shabaninejad, A. & Tafaghodinia, B. (2017a) Evaluation of the ability of LVQ4 artificial neural network model to predict the spatial distribution pattern of Tuta absoluta in the tomato field in Ramhormoz. Journal of Entomolological Society of Iran 36, 195–204.
Shabaninejad, A. & Tafaghodinia, B. (2017b) Evaluation of the Geostatistical and Artificial Neural Network Methods to estimate the Spatial Distribution of Tetranychus urticae (Acari: Tetranychidae) in Ramhormoz Cucumber fields. Journal of Applied Entomology and Pathology 85 (1), 21–29.
Shabaninejad, A., Tafaghodinia, B. & Zandi–Sohani, N. (2017a) Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran. Persian Journal of Acarology 6 (4), 315–328.
Shabaninejad, A., Tafaghodinia, B. & Zandi–Sohani, N. (2017b) Hybrid neural network With genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari.: Tetranychidae) in cucumbers field of Ramhormoz. Persian Journal of Acarology 6, 53–62.
Vakil–Baghmisheh, M.T. & Pavešic, N. (2003a) Premature clustering phenomenon and new training algorithms for LVQ. Pattern Recognition, 36(5), 1901–1921.
Vakil–Baghmisheh, M.T. & Pavešic, N. (2003b). A Fast simplified fuzzy ARTMAP network. Neural Processing Letters 17, 273–301.
Walter, D. E. & Lindquist, E. E. (1997) Australian species of Lasioseius
(Acari: Ascidae): the porulosus group and other species from rainforest canopies. Invertebrate Taxonomy 11(4), 525–547.
Wang, Y. M. & Elhag, T. M. S. (2007) A comparison of neural network, evidential reasoning and multiple regression analysis in modeling bridge risks. Expert Systems with Applications 32(5), 336–348.
Young, P., Ja-Myung, K., Buom-Young, L., Yeongjin & Yooshin, K. (2000) Use of an Artificial Neural Network to Predict Population Dynamics of the Forest–Pest Pine Needle Gall Midge (Diptera: Cecidomyiida). Environmental Entomology 29: 1208–1215.
Yuxin, M., Mulla, D. J. & Pierre, C.R. (2006) Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture 7(2), 117–135.
Zhang, W. J., Zhong, X. Q. & Liu, G. H. (2008) Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stochastic Environmental Research and Risk Assessment 22(8), 207–216.
Zhang, Y. F. & Fuh, J. Y. H. (1998) A neural network approach for early cost estimation of packaging products. Computers & Industrial Engineering 34(4), 433–50.