کاربرد روش الگوریتم ژنتیک در برآورد پارامترهای سری زمانی خطی به منظور پیش‌بینی خشکسالی

Abo-Hammour  ZES, Alsmadi OM, Al-Smadi AM, Zaqout MI and Saraireh MS, 2012. ARMA model order and parameter estimation using genetic algorithms. Mathematical and Computer Modelling of Dynamical Systems 18(2): 201-221.

Allen RG, Pereira LS, Raes D and Smith M, 1998. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and drainage paper 56 FAO, Rome 300:D05109.

Ayalew S, Babu MC and Rao LKM, 2012. Comparison of new approach criteria for estimating the order of autoregressive process. IOSR Journal of Mathematics 1(3): 10-20.

Beligiannis GN, Demiris EN and Likothanassis SD, 2000. Self-adaptive evolution strategies for ARMA model identification. In Signal Processing Conference, 2000 10th European (pp. 1-4). IEEE.

Box GE and Jenkins GM, 1976. Time series analysis: forecasting and control, revised ed. Holden-Day,

Brock WE, Brock WA, Hsieh DA and LeBaron BD, 1991. Nonlinear dynamics, chaos, and instability: statistical theory and economic evidence. MIT press.

Brock W.A, Scheinkman J.A, Dechert WD and LeBaron B, 1996. A test for independence based on the correlation dimension. Econometric Reviews 15: 197-235.

Durdu O.F, 2010.  Application of linear stochastic models for drought forecasting in the Buyuk Menderes river basin, western Turkey. Stochastic Environmental Research and Risk Assessment 24(8): 1145-1162.

Ervural BC, Beyca OF and Zaim S, 2016. Model estimation of ARMA using genetic algorithms: A case study of forecasting natural gas consumption. Procedia-Social and Behavioral Sciences 235: 537-545.

Hassanzadeh Y, Abdi Kordani A, Fakheri Fard A. 2012. Application of meta-heuristic methods in drought monitoring (Case study: Tabriz station). Water and Soil Science- University of Tabriz, 22(3): 29-46. (In Persian)

Holland J.H, 1962. Outline for a logical theory of adaptive systems. Journal of the ACM (JACM) 9(3): 297-314.

Hosseini-Moghari SM and Araghinejad S, 2016. Application of Statistical, Fuzzy and Perceptron Neural Networks in Drought Forecasting (Case Study: Gonbad-e Kavous Station). Water and Soil (Agricultural Sciences and Technology) 30(1): 247-259. (In Persian)

Kempes C, Myers O, Breshears D and Ebersole J, 2008. Comparing response of Pinus edulis tree-ring growth to five alternate moisture indices using historic meteorological data. Journal of Arid Environments 72(4):350-357.

Khalili K, Nazeri Tahrudi M, Abbaszadeh Afshar M and Nazeri Tahrudi Z, 2014. Modeling monthly mean air temperature using SAMS2007 (Case Study: Urmia synoptic station). Journal of Middle East Applied Science and Technology (JMEAST) 15: 578-583.

Kim M and Kim J, 2017. GA-ARMA model for predicting IGS RTS corrections. International Journal of Aerospace Engineering, 2017: 1-7.

Maerufi S, Khatar B, Sadeghifar  M, Parsafar N and Eildurmi A, 2014. Drought prediction using SARIMA time series and SPI index in the central region of Hamedan province. Water Research in Agriculture 28(1): 213-225. (In Persian)

Maerufi S, Saghaei S, Ershadfath F and Khatar  B, 2015. Evaluating time series models to estimate monthly temperature of Iran’s old synoptic stations during 1977-2005. Water and Soil Science - University of Tabriz, 24(4): 215-226. (In Persian)

Michalewicz Z, 1999. Genetic Algorithms + Data Structures = Evolution Programs, Department of Computer Science University of North Carolina, USA. Springer Publishing 388p.

Mishra A, Desai V, 2005. Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment 19(5): 326-339.

Mishra A and Desai V, 2006. Drought forecasting using feed-forward recursive neural network .Ecological Modelling 198(1-2): 127-138.

Mishra A, Desai V and Singh V, 2007. Drought forecasting using a hybrid stochastic and neural network model. Journal of Hydrologic Engineering 12(6): 626-638.

Peng P and Chen Q, 2003. Improved genetic algorithm and application to ARMA modelling Pp. 7-7. In SICE Annual Conference Program and Abstracts SICE Annual Conference 2003. The Society of Instrument and Control Engineers. Fukui, Japan.

Raziei T, 2017. Drought forcasting in eastern and central arid and semi-arid regions of Iran using time series and Markov chainmodels. Watershed Engineering and Management 8(4): 454-477. (In Persian)

Salas JD, 1993. Analysis and modelling of hydrological time series Pp. 19.1-19.72. In: Maidment, D.R., Ed., Handbook of Hydrology, McGraw-Hill, New York.

Salas JD, Delleur JW, Yevjevich V and Lane WL, 1980. Applied Modeling of Hydrologic Time Series. Water Resource Publications, P. O. Box 2841. Littleton, Colorado .80161, U.S.A. 484 P.

Sharifan H. and Ghahraman B, 2007. Evaluation of rainfall forecasting in Golestan province using time series. Agricultural Sciences and Natural Resources 14(3): 196-209(In Persian).

Thornthwaite C.W, 1948. An approach toward a rational classification of climate. Geographical Review 38(1): 55-94.

Toufani P, Mosaedi A and Fakheri Fard A, 2011. Prediction of precipitation applying wavelet network model (case study: Zarringol station, Golestan province, Iran). Water and Soil (Agricultural Sciences and Technology) 25(5): 1217-1226. (In Persian)

Vicente-Serrano SM, Begueria S and Lopez-Moreno J.I, 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate 23(7): 1696-1718.

Zarei AR, Moghimi MM and Mahmodi M.R, 2016. Modeling and prediction of seasonal drought, using RDI index and time series models (Case study: Tehran synoptic station). Journal of Desert Ecosystem Engineering. 5(11): 105-116. (In Persian)