Drought Simulation using Two CEEMD-GPR and GPR-GARCH Integrated Models (Case Study: Northwest of Iran)

Document Type : Research Paper

Authors

1 Professor, Department of Civil Engineering, University of Tabriz, Iran.

2 PhD candidate, Department of Civil Engineering, University of Tabriz, Tabriz, Iran

Abstract

Drought is one of the most important natural disasters affecting agriculture section and water resources. Droughts often occur in arid and semi-arid regions. Therefore, drought forecasting is necessary and plays an important role in the planning and management of water resources. So far, numerous drought prediction methods have been proposed in the literature, including time series models, regression models, probabilistic models, machine learning models, physical models, and a host of hybrid models. Although all of these methods have shown promising results in terms of improving accuracy of drought forecasts, the impact of climate change on droughts has highlighted the need for more advanced methods for predicting this event. Engle (1982)  proposed the ARCH model which can depict the variance of the time series and eliminate the heteroskedasticity caused by the constant time series variance. The GARCH model was further developed based on the ARCH model, the advantage of which is that it can use a simpler form to represent a high-order ARCH model. On the other hand, in recent years, the Meta model approaches have been applied in investigating the hydraulic and hydrologic complex phenomena. Hybrid models involving signal decomposition have also been found to be effective in improving prediction accuracy of time series prediction methods (Amirat et al., 2018). Complementary Ensemble Empirical Mode Decomposition analysis is one of the widely-used signal decomposition methods for hydrological time series prediction. Decomposition of time series reduces the difficulty of forecasting, thereby improving forecasting accuracy.
Due to the complexity of the drought phenomenon and the effect of various parameters on its prediction, in this study, the capability of GPR as a kernel-based approach and also integrated CEEMS-GPR and GPR-GARCH models were assessed for drought modeling based on six-month SPI index for the three cities of Tabriz, Urmia, and Ardabil in Iran during the period 1978-2017. In fact, this study attempts to create a novel method by combining the CEEMD and GARCH models with the GPR to enhance the estimation accuracy of the six- month SPI drought index.

Keywords


1- Agarwal, A., Maheswaran, R., Sehgal, V., Khos, R., Sivakumar, B. and Bernhofer, C., 2016. Hydrologic regionalization using wavelet-based multiscale entropy method. Journal of Hydrology, 538, pp.22–32.
 
2- Amirat, Y., Benbouzidb, M., Wang, T., Bacha, K. and Feld, G., 2018. EEMD-based notch filter for induction machine bearing faults detection. Applied Acoustics, 133, pp.202–209.
 
3- Govindaraju, R.S., 2000. Artificial neural networks in hydrology. I: Preliminary concepts. Hydrologic Engineering, ASCE, 5(2), pp.115-123.
 
4- Engle, R.F., 1982. Autoregressive conditional heteoscedasticity with estimates of the variance of United Kingdom inflations. Econometrica, 50, pp.987-1007.
 
5- Hayes, M.J., 2007. What is drought: drought indices. National drought mitigation center, University of Nebraska. (Online). http://drought. unl. edu/whatis/indices. htm., 2007.
 
6- Hayes, M.J., Svoboda, M.D., Wilhite, D.A. and Vanyarkho. O.V., 1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society, 80(3), pp.429- 437.
 
7- Hung, W.U., Hayes, M.J., Wilhite, D.A. and Svoboda, M. D., 2005. The effect of the length of record on the standardized precipitation index calculation. International journal of climatology, 25, pp.505-520.
 
8- Khosravi, M., Nasiri, M., Safavi, A.A. and Pourjafarian, N., 2014. Drought furcating using artificial noral network, case study: Siraz station. Journal of Geographical Studies of Arid Regions, 2(8), pp.103-119. (In Persian).
 
9- 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. Earth System Science, 15, pp.2401-2419.
 
10- McKee, T.B., Doesken, J. and Kleist, J., 1993. The Relationship of drought frequency and duration to time scales. In Eighth Conference on Applied Climatology, Anaheim, California.
 
11- Modarres, R. and Ouarda, T.B., 2013. Modeling rainfall–runoff relationship using multivariate GARCH model. Journal of Hydrology, 499, pp.1-18.
                                                              
12- Modarres, R. and Ouarda, T.B., 2014. Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. Water Resources Research, 50(1), pp.601-618.
 
13- Modarres, R., Sarhadi, A. and Burn, D.H., 2016. Changes of extreme drought and flood events in Iran. Global and Planetary Change, 144, pp.67-81.
 
14- Morid, S., Smakhtin, V. and Bagherzadeh, K., 2008. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27, pp.2103-2111.
 
15- Morid, S., Smakhtin, V. and Moghaddasi, M., 2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology, 26(7), pp.971-985.
 
16- Neal, R.M., 1997. Monte carlo implementation of gaussian process models for bayesian regression and classification. Technical report, no. 9702.
 
17- Nosrati, K., Eslamian, S., Shahbazi, A., Malekian, A. and Saravi, M.M., 2009. Application of daily water resources assessment model for monitoring water resources indices. International Journal of Ecological Economics and Statistics, 13, pp.88-99.
 
18- Rezazadeh, A. and Sattari, M.T., 2016. Estimation of scour depth of piers in hydraulic structures using Gaussian process regression. Journal of Applied Research in Irrigation and Drainage Structures Engineering, 16(65), pp.19-36 . (In Persian).
 
19- Saada, N. and Abu-Romman, A., 2017. Multi-site modeling and simulation of the standardized precipitation index (SPI) in Jordan. Journal of Hydrology: Regional Studies, 14, pp.83–91.
 
20- Samuelsson, O., Björk, A., Zambrano, J. and Carlsson, B., 2017. Gaussian process regression for monitoring and fault detection of wastewater treatment processes. Water Science and Technology, 75(12), pp.2952-2963.
 
21- Shokrikochak, S. and Behnia, A., 2013. Monitoring and prediction of Khuzestan province, Iran drought using SPI drought index and Markov chain. Irrigation Sciences and Engineering, 36(3), pp.1-12. (In Persian).
 
22- Siviapragasam, C. and Liong, S., 2001. Rainfall and runoff forcasting with SSA-SVM approach. Hydroinformation, 3(5), pp.141-152.
 
23- Wang, W., Van Gelder, P.H. and Vrijling, J.K., 2005. Testing and modeling autoregressive conditional heteroskedasticity of streamflow processes. Nonlinear Processes in Geophysics, 12, pp.55-66.
 
24- Wu, Z. and Huang, N.E., 2004. A study of the characteristics of white noise using the empirical mode 4decomposition method. Proceedings of the Royal Society of London 460A, pp.1597–1611.
 
25- Younesi, M., Shahraki, N., Marofi, S. and Nozari, H., 2018. Drought forecasting using artificial wavelet neural network integrated model (WA-ANN) and time series model (ARIMA). Irrigation Sciences and Engineering, 41(2), pp.167-181. (In Persian).
 
26- Zhu, S., Luo, X., Xu, Z. and Ye, L., 2019. Seasonal streamflow forecasts using mixture-kernel GPR and advanced methods of input variable selection. Hydrology Research, 50(1), pp.200-14.
Volume 44, Issue 1
June 2021
Pages 77-92
  • Receive Date: 08 June 2019
  • Revise Date: 03 December 2019
  • Accept Date: 09 December 2019
  • Publish Date: 21 March 2021