1. Abella, S.R., Covington, W.W. 2006. Vegetation environment relationships and ecological speciesgroups of an arizonaPinus ponderosa landscape, Plant Ecology, 185 (2): 225-268. 2. Aguilera, A.M., Escabias, M., Valderrama, M.J. 2006. Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics & Data Analysis, 50(8): 1905-1924. 3. Ashcroft, M.B. 2006. A method for improving landscape scale temperature predictions and the implications for vegetation modeling. Ecological Modelling, 197(3-4): 394-404. 4. Biglouei, M.H., Akbarzadeh, A., and Yousefi, K. 2008. Effect of composted wood barks (CWBs) on some soil physical and hydraulic properties. International Journal of Applied Agricultural Research, 4(1): 1-14. 5. Borana S.L., Yadav S.K. 2017. Prediction of Land Cover Changes of Jodhpur City Using Cellular Automata Markov Modelling Techniques. International Journal of Engineering Science, 17(11): 15402-15406. 6. Carl, J., Ku hn, I. 2007. Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecological Modelling, 207(2-4): 159-170. 7. Coops, N.C., Waring, R.H., Schroeder, T.A. 2009. Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest, U.S.A. Ecological Modelling, 220(15): 1787-1796. 8. Gorttappeh, A.H., Hasani, M.H.,Ranji, H. 2006. Recognition and ecological investigation of almond species (Amygdalus spp.) in West Azerbaijan province. IV international symposium on pistachios and almond. Acta Hort. (ISHS), 726: 253-258. 9. Jahani A. 2019. Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modeling using regression analysis and artificial neural networks. Journal of Forest Science. 65(2):61-9. 10. Jahani A. 2019. Sycamore failure hazard classification model (SFHCM): an environmental decision support system (EDSS) in urban green spaces. International Journal of Environmental Science and Technology. 16(2):955-64. 11. Jahani, A., Feghhei, J. Makhdoum, M.F. And Omid, M. 2016. Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network. Journal of Environmental Planning and Management, 59(2): 222-244. 12. Kalkhajeh, YK., Arshad, RR., Amerikhah, H., Sami, M. 2012. Comparison of multiple linear regressions and artificial intelligence-based modeling techniques for prediction the soil cation exchange capacity of Aridisols and Entisols in a semi-arid region. Australian Journal of agricultural engineering, 3(2):39. 13. Maier, H., Jain, R.A., Dandy, G.C., Sudheer, K.P. 2010. Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions. Environmental Modelling and Software, 25(8): 891-909 14. Melesse, A.M., Hanley, R.S. 2005. Artificial neural network application for multi-ecosystem carbon flux simulation. Ecological Modeling, 189(3-4): 305-314. 15. Merdun, H., Çınar, Ö., Meral, R.,Apan, M. 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90(1-2):108-116. 16. Moghadam, H.S.,Helbich, M. 2013. Spatiotemporal urbanization processes in the megacity of Mumbai India: A Markov chains-cellular automata urban growth model. Applied Geography, 40: 140–149. 17. Pearce, J., and Ferrier, S. 2000. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecological Modelling, 128(2-3): 127-147. 18. PiriSahragard H, ZareChahouki M. 2015. An evaluation of predictive habitat models performance of plant species in Hozesoltan rangelands of Qom province. Ecological modelling, 309-310:64-71. 19. Tayebi, M.H., Tangestani, M.H., Roosta, H. 2010. Environmental impact assessment using neural network model: A case study of the jahani, Konarsiah and KoheGach salt pluges, SE Shiraz, Iran. Abstract of the 7th ISPRS TC VII Symposium. Austria, 557-562. 20. Tiwari, A., Jain, K. 2014. GIS Steering smart future for smart Indian cities. International Journal of Scientific and Research Publications, 4(8): 442-446. 21. Vali, A., Ramesht, M.H., Seif, A. and Ghazavi,R. 2012. An assessment of the artificial neuralnetworks technique to geomorphologicmodeling sediment yield (case studySamandegan river system). Geography andEnvironmental Planning Journal, 44(4): 5-9. 22. Wang, X.D., Zhong, X.H., Liu, S.Z., Liu, J.G.,Wang, Z.Y. and Li, M.H. 2008. Regionalassessment of environmental vulnerability inthe Tibetan Plateau: development andapplication of a new method. Journal of AridEnvironments, 72(10): 1929-1939. 23. Xing, Y., Horner, R.M.W., El-Haram, M.A., Bebbington, J. 2009. A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3): 209–224. 24. Yijun, L., Jiali, T., Hongfen, J., Guangping, Z., and Zhimin, Y. 2010. Artificial Neural Networks Applied in Environmental Quality Assessment. Chengdu: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT). Agu. 5-6 Tehran, Iran, 156–157. 25. ZareChahouki, MA., KhalasiAhvazi, L., Azarnivand, H. 2012. Comparison of three modeling approaches for predicting plant species distribution in mountainous scrub vegetation (Semnan rangelands, Iran). Polish Journal of Ecology, 60(2):105-117.
|