An Efficient Approach of Focused Time Delay Neural Network in Drought Forecasting in Central Iran

Paper Details

Research Paper 01/07/2016
Views (154) Download (6)

An Efficient Approach of Focused Time Delay Neural Network in Drought Forecasting in Central Iran

Abbasali Vali, Fatemeh Roustaei
J. Bio. Env. Sci.9( 1), 231-244, July 2016.
Certificate: JBES 2016 [Generate Certificate]


An exact prediction and modeling of drought is essential for watersheds management. The main contribution of this research is in the design, performance and comparison of drought forecasting models using Focused Time Delay Neural Networks (FTDNN). The network was trained to perform one-step-ahead predictions. Standardized Precipitation Index(SPI) were applied in various time scales including 3, 6, 9, 12, 18, 24 and 48 monthly time series in 14 synoptic stations in Central Iran during 1965–2014. Five categories of back-propagation training algorithms namely resilient back propagation (RP), batch gradient descent (GD and GDX), Quasi-Newton (BFGS), conjugate gradient (CGF, CGP, and CGB) and Levenberg-Marquardt (LM) were used. Then, according to the best algorithm, the number of neurons in the hidden layer was optimized and the best performance was identified. The number of epochs, high Correlation Coefficient (R2), least Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were considered to evaluate the performance of the FTDNN model at each step. The result showed that the Levenberg-Marquardt (LM) was the best algorithm and node 31 was the most efficient for drought prediction. Finally, the designed network was applied on all of the SPIs time series to determine the best in prediction according to statistical parameters. It was found that better results can be achieved by increasing the duration of the time series. According to the results obtained, FTDNN trained by LM is an efficient tool to model and predict drought events especially in long term time series.


Abramowitz M, Stegun IA. 1965. Handbook of mathematical functions. Dover New York.

Adamowski JF. 2008. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology 3533, 247-266.

Adeoti OA, Osanaiye PA. 2013. Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. International Journal of Computer Applications 6920, 8-12.

Afkhami H, Dasturani M, Malekinejad H, Mobin M. 2010. Effect of climatic factors on accuracy of ann-based drought prediction in yazd area. Water and Soil Science(in Persian) 1451, 157-170.

Aparisi F, Avendaño G, Sanz J. 2006. Techniques to interpret T 2 control chart signals. IIE Transactions 388, 647-657.

Bari Abarghouei H, Zarch M AA, Dastorani MT, Kousari MR, Zarch MS. 2011. The survey of climatic drought trend in Iran. Stochastic Envir-onmental Research and Risk Assessment 256, 851-863.

Belayneh A, Adamowski J. 2012. Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression. Applied Computational Intelligence and Soft Computing 2012, 1-13.

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. Journal of Hydrology 508, 418-429.

Benmahdjoub K, Ameur Z, Boulifa M. 2013. Forecasting of Rainfall using Time Delay Neural Network in Tizi-Ouzou (Algeria). Energy Procedia 36, 1138-1146.

Beven K. 2006. A manifesto for the equifinality thesis. Journal of Hydrology 3201, 18-36.

Charaniya N, Dudul S. 2013. Time Lag recurrent Neural Network model for Rainfall prediction using El Niño indices. International Journal of Scientific and Research Publications 367.

Crone SF. 2004. A business forecasting competition approach to modeling artificial neural networks for time series prediction pp. 207-213.

Dastorani M, Afkhami H. 2011. Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert 161, 39-48.

De Martonne E. 1926. L’indice d’aridité. Bulletin de l’Association de géographes français 39, 3-5.

Demuth H, Beale M, Hagan M. 2008. Neural network toolbox™ 6. User’s guide.

Edossa DC, Babel M S, Gupta AD. 2010. Drought analysis in the Awash river basin, Ethiopia. Water resources management 247, 1441-1460.

Edwards DC. 1997. Characteristics of 20th century drought in the United States at multiple time scales, DTIC Document.

Ghorbani M. 2013. The economic geology of Iran: mineral deposits and natural resources. Springer Science & Business Media.

Hagan MT, Menhaj MB. 1994. Training feedforward networks with the Marquardt algorithm. Neural Networks, IEEE Transactions on 56, 989-993.

Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV. 1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society 803, 429-438.

Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural networks 25, 359-366.

Ike DU, Adoghe A. 2013. Back-Propagation Artificial Neural Network Techniques for~ Optical Character Recognition-A Survey. International Jou-rnal of Computers and Distributed Systems 3II, 1-6.

Illeperuma G, Sonnadara U. 2009. Forecasting Droughts using Artificial Neural Networks. Promoting Knowledge Transfer to Strengthen Disaster Risk Reduction & Climate Change Adaptation 100.

Jamshidi H, Arian A, Rezaeian-Zadeh M. 2011. Drought forecasting by Multilayer Perceptron network in Different climatological regions pp. 15-23.

Kangas RS, Brown TJ. 2007. Characteristics of US drought and pluvials from a high‐resolution spatial dataset. International Journal of Climatology 2710, 1303-1325.

Keskin ME, Terzi O, Taylan ED, Küçükyaman D. 2011. Meteorological drought analysis using artificial neural networks. Scientific Research and Essays 621, 4469-4477.

Kim TW, Valdés JB. 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering 86, 319-328.

Kişi Ö, Uncuoğlu E. 2005. Comparison of three back-propagation training algorithms for two case studies. Indian journal of engineering & materials sciences 125, 434-442.

Lahmiri S. 2011. On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms.

Luk K, Ball J, Sharma A. 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of Hydrology 2271, 56-65.

McKee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time scales pp. 179-183, American Meteorological Society Boston MA USA.

Mishra A, Desai V. 2005. Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment 195, 326-339.

Mokhnache L, Boubakeur A. 2002. Comparison of different back-propagation algorithms used in the diagnosis of transformer oil pp. 244-247.

Moradi Dashtpagerdi M, Kousari MR, Vagharfard H, Ghonchepour D, Hosseini M E, Ahani H. 2014. An investigation of drought magnitude trend during 1975–2005 in arid and semi-arid regions of Iran. Environmental Earth Sciences 733, 1231-1244.

Moreira E, Mexia J, Pereira L. 2012. Are drought occurrence and severity aggravating? A study on SPI drought class transitions using log-linear models and ANOVA-like inference.

N.A.Charaniya SVD. 2013. Time Lag recurrent Neural Network model for Rainfall prediction using El Niño indices. International Journal of Scientific and Research Publications 31, 5.

Naderi M, Raeisi E. 2015. Climate change in a region with altitude differences and with precipitation from various sources, South-Central Iran. Theoretical and Applied Climatology 1243, 529-540.

Noori R, Khakpour A, Omidvar B, Farokhnia A. 2010. Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications 378, 5856-5862.

Rezaeian-Zadeh M, Tabari H. 2012. MLP-based drought forecasting in different climatic regions. Theoretical and Applied Climatology 1093-4, 407-414.

Şenkal O, Yıldız BY, Şahin M, Pestemalcı V. 2012. Precipitable water modelling using artificial neural network in Cukurova region. Environmental monitoring and assessment 1841, 141-147.

Sepulcre-Canto G, Horion S, Singleton A, Carrao H, Vogt J. 2012. Development of a Combined Drought Indicator to detect agricultural drought in Europe. Natural Hazards and Earth System Science 1211, 3519-3531.

Thom HC. 1958. A note on the gamma distribution. Monthly Weather Review 864, 117-122.

Vicente-Serrano SM. 2006. Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula. Water resources management 201, 37-60.

Waibel A. 1989. Modular construction of time-delay neural networks for speech recognition. Neural computation 11, 39-46.

Wilson DR, Martinez TR. 2003. The general inefficiency of batch training for gradient descent learning. Neural networks 1610, 1429-1451.

Xie JX, Cheng CT, Chau KW, Pei YZ. 2006. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. International Journal of Environment and Pollution 283-4, 364-381.

Yazdani M, Saghafian B, Mahdian M, Soltani S. 2009. Monthly runoff estimation using artificial neural networks. Journal of Agricultural Science and Technology 11, 335-362.