Arabian Journal of Geosciences

, Volume 6, Issue 5, pp 1417–1433

Prediction of drought in dry lands through feedforward artificial neural network abilities


  • Hossein Bari Abarghouei
    • Department of AgriculturePayame Noor University
  • Mohammad Reza Kousari
    • Management Center for Strategic Projects, Cadastral Scientific Documentation GroupFars Organization of Jahad-Agriculture
    • Faculty of Natural ResourcesYazd University
Original Paper

DOI: 10.1007/s12517-011-0445-x

Cite this article as:
Bari Abarghouei, H., Kousari, M.R. & Asadi Zarch, M.A. Arab J Geosci (2013) 6: 1417. doi:10.1007/s12517-011-0445-x


Drought is one of the most important natural hazards in Iran. It is especially more prevalent in arid and hyper arid regions where there are serious limitations in regard to providing sufficient water resources. On the other hand, drought modeling and particularly its prediction can play important role in water resources management under conditions of lack of sufficient water resources. Therefore, in this study, drought prediction in a hyper arid location of Iran (Ardakan region) has been surveyed based on the abilities of artificial neural. Standardized Precipitation Index (SPI) in different time scales (3, 6, 9, 12, and 24 monthly time series) computed based on the data gathered from four rain gauge stations. After evaluation and testing of different artificial neural networks (ANN) structures, gradient descent back propagation (traingd) network showed higher abilities than others. Then, the predictions of SPI time series with different monthly lag times (1:12 months) were tested. Generally, drought prediction by ANNs in the Ardakan region has shown considerable results with the correlation coefficient (R) more than 0.79 and in the most cases and it rises more than 0.90, which indicates the ANN’s ability of drought prediction.


Drought managementHyper arid regionPrecipitationSPIWater resources

Copyright information

© Saudi Society for Geosciences 2011