Skip to main content
SpringerLink
Log in

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

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Abramowitz M, Stegun A (1965) Handbook of mathematical formulas, graphs, and mathematical tables. Dover, New York

    Google Scholar 

  • Bordi I, Sutera A (2002) An analysis of drought in Italy in the last fifty years. Nuovo Cimento 025(2):185–206

    Google Scholar 

  • Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Contr Signals Syst 2(4):303–314

    Article  Google Scholar 

  • Dastorani MT, Afkhami H (2011) Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert Journal 16:39–48

    Google Scholar 

  • Edwards D, McKee T (1997) Characteristics of 20th century drought in the United States at multiple timescales. 97–2. Colorado State University Fort Collins, Climatology Report

  • Erol Keskin M, Terzi Ö (2006) Artificial neural network models of daily pan evaporation. Hydrologic Engrg 11(1):65–70

    Article  Google Scholar 

  • Fletcher D, Goss E (1993) Forecasting with neural networks: an application using bankruptcy data. Info Manage 24:59–167

    Google Scholar 

  • Gabriel KR, Neumann J (1962) A Markov chain model for daily rainfall occurrences at Tel Aviv. Q J R Meteorol Soc 88:90–95

    Article  Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrologic Engrg 5(2):124–137

    Article  Google Scholar 

  • Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Menlo Park

    Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 25(5):359–366

    Article  Google Scholar 

  • Hughes B, Saunders M (2002) A drought climatology for Europe. Int J Climatol 22:1571–1592

    Article  Google Scholar 

  • Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4(2):4–22

    Article  Google Scholar 

  • Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022

    Article  Google Scholar 

  • Marzban C, Stumpf GJ (1996) A neural network for tornado and/or severe weather prediction based on Doppler Radar-derived attributes 10th Annual Mid-American Symposium on Emerging Computer Technologies, University of Oklahoma, October 28–29

  • McKee T, Doesken N, Kleist J (1993) The relationship of drought frequency and duration to time scales, 8th conference on applied climatology, Anaheim,CA, Am Meteor Soc, Boston, pp 179–184

  • Mishra AK, Desai VR (2005a) Drought forecasting using stochastic models. Stoch Environ Res Risk Assessment 19:326–339

    Article  Google Scholar 

  • Mishra AK, Desai VR (2005b) Spatial and temporal drought analysis in the Kansabati River Basin, India. Int J River Basin Management 3(1):31–41

    Article  Google Scholar 

  • Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198:127–138

    Article  Google Scholar 

  • Mishra AK, Singh VP (2009) Analysis of drought severity-area-frequency curves using a general circulation model and scenario uncertainty. J Geophys Res 114:D06120. doi:10.1029/2008JD010986

    Article  Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216

    Article  Google Scholar 

  • Mishra AK, Singh VP (2011) Drought modeling—a review. J Hydrol 403(1–2):157–175

    Article  Google Scholar 

  • Mishra AK, Desai VR, Singh VP (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrolog Eng ASCE 12(6):626–638

    Article  Google Scholar 

  • Mishra AK, Singh VP, Desai VR (2009) Drought characterization: a probabilistic approach. Stoch Environ Res Risk Assess 23(1):41–55

    Article  Google Scholar 

  • Morid S, Smakhtin S, Moghaddasi M (2006) Comparison of seven meteorological indices for drought monitoring in Iran. Int J Climatol 26:971–985

    Article  Google Scholar 

  • Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111

    Article  Google Scholar 

  • Palmer W (1965) Meteorological drought, U.S. Department of Commerce Weather Bureau Research, Washington, DC

  • Panu US, Sharma TC (2002) Challenges in drought research: some perspectives and future directions. Hydrol Sci J 47:19–30

    Article  Google Scholar 

  • Rahimi Khoob A (2007) Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci 26(3):253–259

    Article  Google Scholar 

  • Rahimi Khoob A (2008) Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrig Sci 27(1):35–39

    Article  Google Scholar 

  • Rahnama MB, Noury M (2008) Developing of Halil River rainfall-runoff model, using conjunction of wavelet transform and artificial neural networks. Res J Environ Sci 2(5):385–392

    Article  Google Scholar 

  • Tang Z, Fishwick PA (1993) Feedforward neural nets as models for time series forecasting. ORSA J Comput 5(4):374–385

    Article  Google Scholar 

  • Thom H (1958) A note on gamma distribution. Monthly Weather Rev 86:117–122

    Article  Google Scholar 

  • Torranin P (1976) Unpredictability of hydrological drought. In: Proceeding of the Second International Symposium in Hydrology, Fort Collins, Colorado, 595–604

  • Win Mar K, Thu Naing T (2008) Optimum neural network architecture for precipitation prediction of Myanmar. World Academy of Science, Engineering and Technology 48

  • Wong FS (1991) Time series forecasting using back propagation neural networks. Neurocomputing 2:147–159

    Article  Google Scholar 

  • Woong Kim T (2008) Monthly precipitation forecasting using rescaling errors. KSCE J Civ Eng 10(2):137–143

    Google Scholar 

  • Woong Kim T, Valdés JB (2003) A nonlinear model for drought forecasting based on conjunction of wavelet transforms and neural networks. ASCE J Hydrolog Eng 8:319–328

    Article  Google Scholar 

  • Wu H, Svoboda M, Hayes M, Wilhite D, Wen F (2007) ppropriate application of the Standardized Precipitation Index in arid locations and dry seasons. Int J Climatol 27:65–79

    Article  Google Scholar 

  • Zhai L, Feng Q (2008) Spatial and temporal pattern of precipitation and drought in Gansu Province, Northwest China. Nat Hazards 49(1):1–24

    Article  Google Scholar 

  • Zhang BL, Dong ZY (2001) An adaptive neural-wavelet model for short term load forecasting. Electric Power Syst Res 59:121–129

    Article  Google Scholar 

Download references

Acknowledgments

Data used in this study were made available by the Iranian Meteorological Organization. The authors greatly acknowledge the Payame Noor University, Yazd, Iran for providing the financial support for the present study. Furthermore, authors greatly appreciate the technical support of the Management Center for Strategic Projects in Fars Organization Centre of Jahad-Agriculture of Iran.

Author information

Authors and Affiliations

  1. Department of Agriculture, Payame Noor University, Yazd, Iran

    Hossein Bari Abarghouei

  2. Management Center for Strategic Projects, Cadastral Scientific Documentation Group, Fars Organization of Jahad-Agriculture, Shiraz, Iran

    Mohammad Reza Kousari

  3. Faculty of Natural Resources, Yazd University, Yazd, Iran

    Mohammad Amin Asadi Zarch

Authors
  1. Hossein Bari Abarghouei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Kousari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Amin Asadi Zarch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Amin Asadi Zarch.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bari Abarghouei, H., Kousari, M.R. & Asadi Zarch, M.A. Prediction of drought in dry lands through feedforward artificial neural network abilities. Arab J Geosci 6, 1417–1433 (2013). https://doi.org/10.1007/s12517-011-0445-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-011-0445-x

Keywords

Search

Navigation