Drought Prediction Using GEP-GARCH Hybrid Model (Case Study: Salmas Synoptic Station)

Document Type : Research Paper


1 Ph.D students in Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

2 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

3 Associate professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

4 Assistant Professor, Department of Civil Engineering, Faculty of Civil Engineering, Urmia University of Technology, Urmia, Iran


Drought prediction plays an important role in designing drought adaptation systems and implementation of relief operations. Hydrological data is a combination of a definite and random section. Given the fact that the production data of intelligent models are definite, application of a new approach, using the random part in predicting this data can increase the certainty of the model. In this research, it was attempted to provide a hybrid model for prediction of drought using a combination of the Gene Expression Programming model (GEP) and the Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) time series model. For this purpose, drought prediction in Salmas station using SPEI drought index at different time scales was investigated during 35 years statistical period and with 5 different input models. The results showed that the GEP method does not have the appropriate accuracy in short-term time scale of SPEI index and it will be improved with increasing time scale. The results of the hybrid model showed that the error of GEP model decreases in all time scales, and this performance improvement is more tangible in the short-time scales, so that the correlation coefficient in three-month time scale in the GEP model has increased from 0.622 to 0.891 in the hybrid model.


Main Subjects

Abbaszadeh Afshar, M., Khalili, K. and Behmanesh, J. (2016).Application of Combined AR-ARCH model in Forecasting Urmia Lake Water Level. Water and Soil Science, 25(4/2), 175-184. (In Farsi)
Allen R.G., Pereira L.S., Raes D. and Smith M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and drainage paper. 56 FAO, Rome 300:D05109.
Bera, A. K. and Higgins, M. L. (1993). ARCH models: properties, estimation and testing. Journal of economic surveys, 7(4), 305-366.
Bollerslev, T. (1986). Generalized autoregressive heteroskedasticity. J. Econom. 52, 307-327.
Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1-2), 5-59.
Baba Ali, H.R. and Dehghani, R. (2017). Compare intelligent models to Estimate monthly Precipitation Kakareza Basian. Iranian journal of Ecohydrology, 4(1), 1-11. doi: 10.22059/ije.2017.60911. (In Farsi)
Behmanesh, J. and Mehdizadeh, S. (2017). Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region. Environmental Earth Sciences, 76(2), 76.
Caiado, J. (2007). Forecasting water consumption in Spain using univariate time series models. 415-423.
Danandeh Mehr, A., Kahya, E. and Ozger, M. (2014). A gene–wavelet model for long lead time drought forecasting. Journal of Hydrology, 517, 691-699.
DaSilva, V.d.P.R. (2004). On climate variability in Northeast of Brazil. Journal of Arid Environments, 58(4),575-596.
 Dastorani, M., & Afkhami, H. (2011). Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert, 16(1), 39-48. (In Farsi)
Dorado, J., RabuñAL, J. R., Pazos, A., Rivero, D., Santos, A. and Puertas, J. (2003). Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Applied Artificial Intelligence, 17(4), 329-343.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Ferreira, C. (2002). Gene expression programming in problem solving. In Soft computing and industry (pp. 635-653). Springer, London.
Guven, A. (2009). Linear genetic programming for time-series modelling of daily flow rate. Journal of earth system science, 118(2), 137-146.
Hosseini-Moghari, S. M. and Araghinejad, S. (2015). Monthly and seasonal drought forecasting using statistical neural networks. Environmental Earth Sciences, 74(1), 397-412.
Khalili, K., Fakheri Fard, A., Dinpaghoh, Y., Ahmadi, F. and Behmanesh, J. (2013) aIntroducing and Application of Combined BL-ARCH Model for Daily River flow Forecasting (Case study: Shahar-Chai River). Journal of Water and Soil, 27(2), 342-350. (In Farsi)
Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H. and Muttil, N. (2001). Genetic Programming and its Application in Real-time Runoff Forecasting. JAWRA Journal of the American Water Resources Association, 37(2), 439-451.
Kisi, O., Shiri, J. and Nikoofar, B. (2012). Forecasting daily lake levels using artificial intelligence approaches. Computers & Geosciences, 41, 169-180.
Laux, P., Vogl, S., Qiu, W., Knoche, H. R. and Kunstmann, H. (2011). Copula-based statistical refinement of precipitation in RCM simulations over complex terrain. Hydrology and Earth System Sciences, 15(7), 2401-2419.
Maca P. and Pech P (2016) Forecasting SPEI and SPI drought indices using the integrated artificial neural networks. Computational intelligence and neuroscience, 2016(14).
Mehdizadeh, S., Behmanesh, J. and Saadatnejad Gharahassanlou, H. (2016). Evaluation of gene expression programming and Bayesian networks methods in predicting daily air temperature. Journal of Agricultural Meteorology, 4(2), 1 -13. (In Farsi)
Mishra, A. and 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.
Naveh, H., Khalili,K ., Alami,M. and Behmanesh,J . (2012). Forecasting River flow By Bilinear Nonlinear Time Series Model (Case Study: Barandoz-Chay & Shahar-Chai rivers). Journal Of Water And Soil, 26(5), 1299-1307. (In Farsi)
Nazeri Tahrodi, M. and Khalili, K. (2015). Comparing Combined ARMA-PARCH and ARMA-ARCH Models for Modeling Peak Flow Discharge (Case Study: Siminehrood River in the West Azarbaijan Province). Water and Soil Science, 25(1-4), 113-127. (In Farsi)
Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical review. 38(1):55-94.
Vicente-Serrano, S.M, 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.
Wang, W., Van Gelder, P., Vrijling, J. and Ma, J. (2005). Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes. Nonlinear processes in Geophysics, 12(1), 55-66.
Yang, Y., Dong, Y., Chen, Y. and Li, C. (2014). Intelligent optimized combined model based on GARCH and SVM for forecasting electricity price of New South Wales, Australia. Paper presented at the Abstract and Applied Analysis.
Zareh Amini, F., Ghorbani, M.A. and Darbandi, S. (2014). Evaluation of Genetic Programming in Estimation of Soil Temperature. Geographical Space. 47(4), 19-38. (In Farsi)