Amani, M. (2004) Urban trees and around the urban. Magazine of Greenspace of parks organization and Greenspace of Tehran 6, 20-27 (in Persian).
Asakereh, H. (2008) Application of Kriging method for rainfall interpolation, case study, rainfall interpolation 17.3.1998 in Iran. Geography and Development Magazine 42, 12-25 (in Persian).
Atashpaz-Gargari, E. (2009) Imperialist Competitive Algorithm development and it is applications, M.S. Thesis, University of Tehran (in Persian).
Azadeh, A., Ghaderi, S. F. & Sohrabkhani, S. (2006) Forecasting electrical consumption by integration of Neural Network, time series and ANOVA. Applied Mathematics and Computation 186, 1753-1761.
Bevan, D. (1984) Orthotomicus erosus (Wollaston) in Usutupine plantations, Swaziland. Forest Research Report No. 64. Usutu Pulp Company Limited, Bhunya, Swaziland.
Castera, I. & Boyd, M. (1996) Designing an Artificial Neural Network for forecasting financial and economic time series. Neurocomputing 12(5), 13-19.
Choudhury, S. K. & Bartarya, G. (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. International Journal of Machine Tools & Manufacture 10(2), 747-753.
Dayhoff, J. E. (1990) Neural network principles. Prentice-Hall International, USA.
Eglitis, A. (2000) Exotic forest pest information system for north america: Orthotomicus erosus. North American Forest Commission.
Enayatifar, R., Sadaei, H. J., Abdullah, A. H. & Gangi, A. (2013) Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting. Energy Conversion and Management 76(3), 1104-1116.
Freeman, J. & Sakura, D. (2005) Neural networks: Algorithms, applications, and programming techniques. Addison-Wesley, Berlin.
Gevery, M. & Worner, S. P. (2006) Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method. Journal of economicentomology 99(3), 979-986.
Gonzalez, R., Grégoire, J. C., Drumont, A. & De Windt, N. (1996) A sampling technique to estimate within-tree populations of pre-emergent Ips typographus (Col., Scolytidae). Journal of Applied Entomology 120, 569-576.
Goovaets, P. (1997) Geostatictics for natural resources evaluation. 1th ed. 512 pp. Oxford University Press.
Gotway, C. A., Ferguson, R. B, Hergert, G. W. & Peterson, T. A. (1996) Comparison of kriging and inverse distance methods for mapping soil parameters. Soil Science Society America Journal 60(13), 1237-1247.
Gressie, N. (1993) Statistics for spatial data. 1th ed. 550 pp. John Wiley, New York.
Haack, R. A. (2004) Orthotomicus erosus: A new pine-infesting bark beetle in the United States. Newsletter of the Michigan Entomological Society 49, 3-4.
Habashi, H., Hosseini, M., Mohammadi, J. & Rahmani, R. (2007) Geostatistic applied in forest soil studying process. Journal of Agricultural Science and natural Resources 14, 1–10. [In Persian with English summary]
Hassani Pak, A. (2007) Geostatistics. 3th ed. 538 pp. University of Tehran Press. [In Persian]
Heykin, S. (1999)Neural networks; A comprehensive foundation. 2th ed. pp 14-29.
Journel, A. G. & Huijbregts, C. J. (1978) Mining geostatistics. 1th ed. 599 pp. Academic Press.
Katherine, A. R. (2001) Geostatistic using SAS software. Owen analytic inc. Deep. River, CT., 6 pp.
Kaul, M., Hill, R. L. & Walthall, C. (2005)Artificial neural networks for corn and soybean yield prediction. Agriculture system 85, 1-18.
Krige, D. G. & Magri, E. J. (1982) Studies of the effects of outliers and data transformation on variogram estimates for a base metal and a gold ore body. Mathematical Geology 14, 557–567.
Lee, J. C., Smith, S. L. & Seybold, S. J. (2005)Mediterranean pine engraver. USDA-APHIS Pest Alert R5-PR-016. 4 pp.
Mahhou, A. & Dennis, F. G. (1992) The almond trees in Morocco. HortTechnology 2, 488-492.
MATLAB 8.0 AND Statistics Toolbox 8.1. (2013) The MathWorks, Inc., Natick, Massachusetts, United States.
Mouton, M., Wingﬁeld, M. J., Van Wyk, P. S. & Van Wyk, P. W. J.
(1994) Graphium pseudormiticum
sp. nov.: a new hyphomycete with unusual conidiogenesis. Mycological Research
Sarmadian, F., Keshavarzi, A., Odagiu, A., Zahedi, Gh. & Javadikia, H. (2014) Mapping of spatial variability of soil organic carbon based on radial basis functions method. ProEnvironment 7, 3-9.
Shabaninejad, A., Tafaghodinia, B. & Zandi Sohani, N. (2017) Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran. Persian Journal of Acarology 6(1), 53-62.
Shoji, T. & Kitaura, H. (2006) Statistical and geostatistical analysis of rainfall in central Japan. Computers and Geosciences 32, 1007-1024.
Shu-Heng, C. (2002) Genetic Algorithm and Genetic Programming in Computational Finance. Springer Kluwer Acadamic Publications, NewYork, USA.
SPSS. (2014)SPSS base 16.0 user’s guide. SPSS Incorporation, Chicago, IL.
Tonnang, Z. E. H., Nedorezov, L. V., Ochanda, H., Owino, J. O. & Lohr, B. (2010) Assessing the impact of biological control of Plutella xylostella through the application of Lotka–Volterra model. Ecological Modeling 220, 60–70.
Torrecilla, J. S., Otero, L. & Sanz, P. D. (2004) A neural network approach for thermal/pressure food processing. Food Engineer 62, 89-95.
Vakil-Baghmisheh, M. T. & Pavešic, N. (2003) Premature clustering phenomenon and new training algorithms for LVQ. Pattern recognition 36(5), 1901-1921.
Vellido, A., Liboa, P. J. G. & Vaughan, J. (2010) Neural networks in business: a survey of applications. Expert Systems with Application 19(3), 12-24.
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, 336-348.
Yang, L. N., Peng, L., Zhang, L. M., Zhang, L. L. & Yang, S. S. (2009) A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis. Computers andElectronics in Agriculture 200-206.
Young-S, P., Ja-Myung, K., Buom-Young, L., Yeong, J. & 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, 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(2), 433-50.
Zhou, X. D., de Beer, Z. W., Wingﬁeld, B. D. & Wingﬁeld, M. J. (2001) Ophiostomatoid fungi associated with three pine-infesting bark beetles in South Africa. Sydowia 53, 290-300.