Evaluating the Performance of Time-Series, Neural Network and Neuro-Fuzzy Models in Prediction of Meteorological Drought (Case study: Semnan Synoptic Station)

Document Type : Research Paper


1 MSc., Faculty of Civil Engineering, Semnan University, Semnan, Iran.

2 Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran.

3 Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran


Drought phenomenon is one of the natural and creeping disasters, which occurs in almost every climate and its properties vary spatially. A considerable number of scientific research has been done on drought in Iran and throughout the world. These studies have examined various aspects of drought. Through such research and knowledge effective and efficient solutions could be found to deal with good management of drought. Since Iran is located in an arid region of the world, nowhere in the country is immune from this phenomenon. This research has attempted to present appropriate models to predict drought for the city of Semnan, Iran.


Main Subjects

1- Aghajani, N., 2014. Torq River sediment prediction in Khorasan Razavi province using multiple and intelligent regression models. Thesis, Semnan University of Semnan, Iran. 140p. (In Persian).
2- Anonymous, 2014. Meteorological system of Semnan province, http://www.semnanweather.ir/index.php
3- Bacanli, U., Firat, M., and Dikbas, F., 2008. Adaptive neuro-fuzzy inference system (ANFIS) for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23(8), pp. 1143-1154.
4- Box, G.E.P., Jenkins, G.M., and Reinsel, G.C., 2002. Time Series Analysis: Forecasting and Control. Fourth Edition, John Wiley Publication, 734 p.
5- Dibike, Y.B., Solomatine, D., and Abbott, M.B., 1999. On the encapsulation of numerical-hydraulic models in artificial neural networks. Journal of Hydraulic Research, 37(2), pp. 147-161.
6- Eivazi, M., Mosaedi, A., and Dehghani, A.A., 2009. comparisonof different approaches predicting SPI. Journal of Water and Soil Conservation, 16(2), pp. 145-167. (In Persian).
7- Jang, J.S.R., 1993. ANFIS: Adaptive-network based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), pp. 665-685.
8- Kamruzzaman, J., Begg, R., and Sarker, R., 2006. Artificial Neural Networks in Finance and Manufacturing. Idea Group Publishing, USA.
9- Karamooz, M., and Araghinezhad, SH., 2010. Advanced Hydrology. Second Edition, Amirkabir University of Technology Press, Tehran. (In Persian).
10- Komasi, M., Alami, M. T., and Nourani, V., 2012. Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering. Journal of Water and Wastewater, 4, pp. 90-102. (In Persian).
11- Mahdavi, M., 2006. Applied hydrology. Sixth Edition, University of Tehran. (In Persian).
12- McKee, T.B., Doesken, N.J., and Kleist, J., 1993. The relation of drought frequency and duration to time scales. 8th Conference on Applied Climatology, 17-22 Jan., Anaheim, California, pp. 379-384.
13- Mishra, A.K., and Desai V.R., 2006. Drought forecasting using feed-forward recursive neural networks. Journal of Ecological Modelling, 98, pp. 127-138.
14- Negaresh, H., and Aramesh, M., 2011. Drought forecast for Khash city using neural network model. Journal of Arid Regions Geographic Studies, 2(6), pp. 33-50. (In Persian).
15- Shirmohammadi, B., Moradi, H.R., Moosavi, V., Taie Semiromi, M., and Zeinali, A., 2013. Forecasting of meteorological drought using wavelet-ANFIS hybrid model for different time steps (Case study: Southeastern part of east Azerbaijan province, Iran). Journal of Natural Hazards, 69, pp. 389-402.
16- Shumway R.H., and Stoffer, D.S., 2006. Time Series Analysis and its Applications: with R Examples. Springer Texts in Statistics, 656 p.