Abstract
In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI t , SDI t − 1, SDI t − 2, SDI t − 3, and SDI t − 4 monthly delays. Two variables including SDI t and SDI t − 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI3, scenario 1 of SDI6, and scenarios 2 and 3 of SDI24 represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.
Similar content being viewed by others
References
Adamowski JF (2008) Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J Hydrol 353(3):247–266
Ahmadi A, Han D, Karamouz M, Remesan R (2009) Input data selection for solar radiation estimation. Hydrol Process 23(19):2754–2764
Al-Faraj FA, Scholz M, Tigkas D (2014) Sensitivity of surface runoff to drought and climate change: application for shared river basins. Water 6(10):3033–3048
Asagha EN, Udo SO, Echi IM (2014) Modeling and simulation of global solar radiation in Warri, Nigeria using gamma test and artificial neural network algorithms. Int J Innov Res Dev ISSN 2278–0211
Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429
Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320(1):18–36
Bordi I, Sutera A (2007) Drought monitoring and forecasting at large scale. In: methods and tools for drought analysis and management. Springer, pp 3–27
Chang F-J, Tsai Y-H, Chen P-A, Coynel A, Vachaud G (2015) Modeling water quality in an urban river using hydrological factors—data driven approaches. J Environ Manag 151:87–96
Cimen M (2008) Estimation of daily suspended sediments using support vector machines. Hydrol Sci J 53(3):656–666
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge university press
Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrol Sci J 43:47–66
Dehghani M, Saghafian B, Nasiri Saleh F, Farokhnia A, Noori R (2014) Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation. Int J Climatol 34(4):1169–1180
Durrant PJ (2001) winGamma TM: a non-linear data analysis and modelling tool with applications to flood prediction. Citeseer
Ebrahimi R, Zahraie B, Nasseri M (2011) Mid-term prediction of meteorological drought using fuzzy inference systems. Water Wastewater J (Full text in Persian, abstract in English), 2(78):112–125
Eskandari DH, Zehtabian GR, Khosravi H, Azareh A (2015) Analysis of temporal and spatial relationship between meteorological and hydrological drought in Tehran province. Geograph Data J (Full text in Persian, abstract in English) 24(96):113–120
Feng Q, Wen X, Li J (2014) Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manag 29(4):1–17
Ganguli P, Reddy MJ (2014) Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach. Hydrol Process 28(19):4989–5009
Ghabaei Sough M, Mosaedi A, Hesam M, Hezarjaribi A (2010) Evaluation effect of input parameters preprocessing in artificial neural networks (Anns) by using stepwise regression and gamma test techniques for fast estimation of daily evapotranspiration. Journal of Water and Soil 24(3):610–624
Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (2014) Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS. Expert Syst Appl 41(11):5267–5276
Gunn SR (1998) Support vector machines for classification and regression ISIS technical report 14
Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8
Keshavarz M, Karami E, Vanclay F (2013) The social experience of drought in rural Iran. Land Use Policy 30(1):120–129
Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205
Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399(1):132–140
Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62
Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51(4):599–612
Mehr AD, Kahya E, Özger M (2014) A gene–wavelet model for long lead time drought forecasting. J Hydrol 517:691–699
Mishra A, Desai V, Singh V (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrol Eng 12(6):626–638
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1):202–216
Moghaddamnia A, Gousheh MG, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32(1):88–97
Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using Artificial Neural Networks and time series of drought indices. Int J Climatol 27(15):2103–2111
Nalbantis I (2008) Evaluation of a hydrological drought index. European Water, EWRA Publications, 23/24:67–77
Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23(5):881–897
Noori R, Karbassi A, Moghaddamnia A, Han D, Zokaei-Ashtiani M, Farokhnia A, Gousheh MG (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401(3):177–189
Özger M, Mishra AK, Singh VP (2012) Long lead time drought forecasting using a wavelet and fuzzy logic combination model: a case study in Texas. J Hydrometeorol 13(1):284–297
Remesan R, Mathew J (2014) Hydrological data driven modelling: a case study approach vol 1. Springer
Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol 109(3–4):407–414
Sharma T, Panu U (2014) Predicting return periods of hydrological droughts using the Pearson 3 distribution: a case from rivers in the Canadian prairies. Hydrol Sci J 60(10):1783–1796
Smakhtin SMV, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111
Stefánsson A, Končar N, Jones AJ (1997) A note on the gamma test. Neural Computing & Applications 5(3):131–133
Vapnik V (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
Vicente-Serrano SM, López-Moreno JI, Beguería S, Lorenzo-Lacruz J, Azorin-Molina C, Morán-Tejeda E (2011) Accurate computation of a streamflow drought index. J Hydrol Eng 17(2):318–332
Vogel R, Wilson I (1996) Probability distribution of annual maximum, mean, and minimum streamflows in the United States. J Hydrol Eng 2(69):69–76. doi:10.1061/(ASCE)1084-0699(1996)1
Wilhite DA (1993) The enigma of drought. In: Drought assessment, management, and planning: theory and case studies. Springer, pp 3–15
Wu MC, Lin GF, Lin HY (2014) Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map. Hydrol Process 28(2):386–397
Yu P-S, Chen S-T, Chang I-F (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3):704–716
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Topical Collection on Water Resources in Arid Areas.
Rights and permissions
About this article
Cite this article
Borji, M., Malekian, A., Salajegheh, A. et al. Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN). Arab J Geosci 9, 725 (2016). https://doi.org/10.1007/s12517-016-2750-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-016-2750-x