ستاری، محمدتقی، و رضازاده جودی، علی (1397). مدلسازی رواناب ماهانه با استفاده از روشهای دادهکاوی بر اساس الگوریتمهای انتخاب ویژگی. حفاظت منابع آب و خاک، 7(4)، 39-54.
سلگی، اباذر، زارعی، حمید، شهنی دارابی، مهرنوش، و علیدادی ده کهنه، صابر (1397). کاربرد مدلهای برنامهریزی بیان ژن و ماشین بردار رگرسیونی جهت مدلسازی و پیشبینی بارش ماهانه. تحقیقات کاربردی علوم جغرافیایی، 18(50)، 91-103. doi:10.29252/jgs.18.50.91
عساکره، حسین، و بیات، علی (1392). تحلیل روند و چرخه ویژگیهای بارندگی سالانة زنجان. جغرافیا و برنامهریزی، 17(45)، 121-142. doi:10.1016/j.atmosres.2015.02.010
کاظمزاده، مجید، ملکیان، آرش، مقدمنیا، علیرضا، و خلیقی، شهرام (1398). ارزیابی اثرات تغییر اقلیم بر خصوصیات هیدرولوژیکی حوزه آبخیز (مطالعه موردی: حوزه آبخیز آجی چای). علوم و مهندسی آبخیزداری ایران، 13(45)، 1-11. dor:20.1001.1.20089554.1398.13.45.1.5
Aftab, S., Ahmad, M., Hameed, N., Bashir, M.S., Ali, I., & Nawaz, Z. (2018). Rainfall prediction in Lahore City using data mining techniques. International Journal of Advanced Computer Science and Applications, 9(4), 254-260. doi:10.1016/j.enbuild.2015.09.073
Amiri, S.S., Mottahedi, M., & Asadi, S. (2015). Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the US. Energy and Buildings, 109, 209-216. doi:10.2307/1267603
Andrews, D.F. (1974). A robust method for multiple linear regression. Technometrics, 16(4), 523-531.
Asakareh, H., & Bayat, A. (2013). The analysis of the trend and the cycles of annual precipitation characteristics of Zanjan. Geography and Planning, 17(45), 121-142. [In Persian]
Baeriswyl, P.A., & Rebetez, M. (1997). Regionalization of precipitation in Switzerland by means of principal component analysis. Theoretical and Applied Climatology, 58(1), 31-41. doi.org/10.1007/BF00867430
Balafoutis, C.J. (1991). Principal component analysis of Albanian rainfall (No. RefW-15-14613). Aristotle University of Thessaloniki.
Cattell, R.B. (1966). The Scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. doi:10.1207/s15327906mbr0102_10
Danandeh Mehr, A.D. (2018). Month ahead rainfall forecasting using gene expression programming. American Journal of Earth and Environmental Sciences, 1(2), 63-70.
Dutta, P.S., & Tahbilder, H. (2014). Prediction of rainfall using data mining technique over Assam. Indian Journal of Computer Science and Engineering (IJCSE), 5(2), 85-90.
Ferreira, C. (2002). Gene expression programming in problem solving. Pp. 635-653, In: Soft computing and industry, Springer, London.
Ghajarnia, N., Liaghat, A., & Arasteh, P.D. (2015). Comparison and evaluation of high-resolution precipitation estimation products in Urmia Basin-Iran. Journal of Water and Soil Resources Conservation, 4(1), 91-109. doi:10.1016/j.atmosres.2015.02.010
Hasan, N., Nath, N.C., & Rasel, R.I. (2015). A support vector regression model for forecasting rainfall. 2nd International Conference on Electrical Information and Communication Technologies (EICT), Pp. 554-559.
Jolliffe, I.T. (1993). Principal component analysis: a beginner's guide-II. Pitfalls, myths and extensions. Weather, 48(8), 246-253. doi:10.1002/j.1477-8696.1993.tb05899.x
Jolliffe, I.T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. doi:10.1098/rsta.2015.0202
Kazemzadeh, M., Malekian, A., Moghaddamnia, A. R., & Sigaroudi, K. (2019). Evaluation of Climate Change Impacts on Hydrological Characteristics of Watershed (Case study: Aji-Chai Watershed). Iranian Journal of Watershed Management Science and Engineering, 13(45), 1-11. dor:20.1001.1.20089554.1398.13.45.1.5 [In Persian]
Krzywinski, M., & Altman, N. (2015). Multiple linear regression. Nature Methods, 12(12), 1103-1104. doi:10.1038/nmeth.3665
Lu, K., & Wang, L. (2011). A novel nonlinear combination model based on support vector machine for rainfall prediction. 4th International Joint Conference on Computational Sciences and Optimization, Pp. 1343-1346.
Mirabbasi, R., Kisi, O., Sanikhani, H., & Gajbhiye Meshram, S. (2019). Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Computing and Applications, 31(10), 6843-6862. doi:10.1007/s00521-018-3519-9
Nolan, B.T., Fienen, M.N., & Lorenz, D.L. (2015). A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. Journal of Hydrology, 531, 902-911. doi:10.1016/j.jhydrol.2015.10.025
Pai, P.F., & Hong, W.C. (2007). A recurrent support vector regression model in rainfall forecasting. Hydrological Processes, 21(6), 819-827. doi:10.1002/hyp.6323
Piña-Monarrez, M.R., & Ortiz-Yañez, J.F. (2015). Weibull and lognormal Taguchi analysis using multiple linear regression. Reliability Engineering & System Safety, 144, 244-253. doi:10.1016/j.ress.2015.08.004
Preacher, K.J., Curran, P.J., & Bauer, D.J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31(4), 437-448. doi:10.3102/10769986031004437
Sattari, M. T. & Rezazadeh Judi, A. (2018). Monthly runoff modeling using data mining methods based on feature selection algorithms. Protection of Water and Soil Resources, 7(4), 39-54. [In Persian]
Solgi, A., Zarei, H., Shahni, D.M., & Alidadi, D.K.S. (2018). Application of gene expression programming and support vector regression models to modeling and prediction monthly precipitation. Journal of Geographical Sciences, 18(50), 91-103. doi:10.29252/jgs.18.50.91 [In Persian]
Sneyers, R., Vandiepenbeeck, M., & Vanlierde, R. (1989). Principal component analysis of Belgian rainfall. Theoretical and applied Climatology. 39(4), 199-204. doi:10.1007/BF00867948
Stathis, D., & Myronidis, D. (2009). Principal component analysis of precipitation in Thessaly region (Central Greece). Global Network of Environmental Science and Technology Journal, 11(4), 467-476.
Steiner, D. (1965). A Multivariate Statistical Approach to Climatic Regionalization and Classification. EJ Brill.
Sureh, F.S., Sattari, M.T., & İrvem, A. (2019). Estimation of monthly precipitation based on machine learning methods by using meteorological variables. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi, 24, 149-154.
Swain, S., Patel, P., & Nandi, S. (2017). A multiple linear regression model for precipitation forecasting over Cuttack district, Odisha, India. The 2nd International Conference for Convergence in Technology, Pp. 355-357. doi:10.1109/I2CT.2017.8226150
Tripathi, S., Srinivas, V.V., & Nanjundiah, R.S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330(3-4), 621-640. doi:10.1016/j.jhydrol.2006.04.030
Vapnik, V.N., & Chervonenkis, A.Y. (2015). On the uniform convergence of relative frequencies of events to their probabilities. Pp. 11-30, In: Vovk, V., Papadopoulos, H., Gammerman, A. (eds) Measures of Complexity. Springer, Cham. doi:10.1007/978-3-319-21852-6_3
Whetton, P.H. (1988). A synoptic climatological analysis of rainfall variability in southeastern Australia. Journal of Climatology, 8(2), 155-177. doi:10.1002/joc.3370080204
Wilks, D.S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Academic press.
Willmott, C.J. (1978). P-mode principal components analysis, grouping and precipitation regions in California. Archives for Meteorology Geophysics and Bioclimatology Series B Theoretical and Applied Climatology, 26(4), 277-295. doi:10.1007/BF02243232
Zaw, W.T., & Naing, T.T. (2008). Empirical statistical modeling of rainfall prediction over Myanmar. International Journal of Computer and Information Engineering, 2(10), 3418-3421. doi:10.5281/zenodo.1084254