Monitoring and Prediction of Drought in Western Urmia Lake Basin Rain Gage Stations by ANFIS Model

Document Type : Research Article


1 Department of Soil Science, Faculty of Agriculture, Urmia University

2 Department of Water Engineering, Islamic Azad University, Sanandaj Branch

3 Department of Water Engineering, University of Tabriz


Drought as a climatic phenomenon affected many different environmental issues and generally is associated with the decreasing in average precipitation. Evaluation and monitoring of the drought is a fundamental step in proper programming of water resources management. Regarding the recent conditions water scarcity in the Urmia Lake basin, assessment of the drought index in this region is inevitable. This study was conducted to evaluation of the SPI index in western parts of the Urmia Lake basin. Rainfall data were collected from 11 rain gage stations in Tabriz and Maragheh plains. The standardized precipitation index in time scales of 9, 12, 24 months used for studying the drought features and adaptive neuro-fuzzy inference system model was applied for prediction of drought. The results indicated that the most serious (-4.07) drought was occurred in Bonab station at the 9 month time scale in October 1984. The longest drought periods were occurred in Heravi, Saiedabad, and Maragheh stations and the shortest drought periods were occurred in Zinjab, Tabriz, and Lighvan stations. The results of SPI prediction by ANFIS model indicated that the model desirably could predict the drought conditions in the study area. The highest value of the coefficient of the determination of ANFIS model was 0.829 for Maragheh station at 24 months time scale and the lowest values of r2 was 0.480 for Saiedabad station at 9 months time scale. Results also indicated that the capability of the ANFIS tend to be better at long time scales.


Main Subjects

  1. منابع

    1. Alizadeh A. Fundamentals of the Applied Hydrology. Ferdowsi University of Mashhad Press; 2010 [Persian]
    2. Akbarzadeh Y, Sadeghi F, Hossein-Babaie M. Spatial analysis of the SPI in east Azarbayjan province during the 1987-2006 period. Regional Congress of the Water and drought; 2009 [Persian]
    3. Asadi E, Majnonihris A, Fakherifard A, Sadredini AA. Evaluation of drought in East Azarbaijan province using SPI index. 2th National Conference on the drought management strategies; 2009 [Persian]
    4. Asghari-Moghaddam A, Fijani E, Nadiri A. Optimization of DRASTIC model by artificial intelligence for groundwater vulnerability assessment in Maragheh-Bonab plain. Journal of Geosciences. 2015; 94:169-176 [Persian]
    5. Araghinejad SH, Karamooz M. Advanced Hydrology. Amir-Kabir University Press; 2010 [Persian]
    6. Azhdari-Moghadam M, Khosravi M, Pourniknam H, Jafari E. Drought prediction by Neuro-Fuzzy model, climatic indices, precipitation, and drought index (Case study: Zahedan). Iranian Journal of Geography and Development. 2012;10(1), 61-72 [Persian]
    7. Bacanli UG, Firat M, Dikbas F. Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment. 2009; 23(8):1143-54.
    8. Bonaccorso B, Bordi I, Cancelliere A, Rossi G, Sutera A. Spatial variability of drought: an analysis of the SPI in Sicily. Water resources management. 2003; 17(4):273-96.
    9. Chang FJ, Chang YT. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources. 2006; 29(1):1-0.
    10. Edwards DC. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report Number 97-2, Colorado State University, Fort Collins, Colorado; 1997
    11. Farrokhnia A, Morid S, Ghaemi H. Data mining on large scale climatic signals for predicting long time drought. Third Congress on Water Resources Management. Tabriz; 2008 [Persian]
    12. Firat M, Güngör M. Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system. Hydrological Processes. 2008; 22(13):2122-32.
    13. Goldust A, Sobhani B. Studying drought and evaluating its prediction possibility in Ardabil province by using SPI index and ANFIS model. Geographical Research. 2015; 30(1):135-152 [Persian]
    14. He B, Lü A, Wu J, Zhao L, Liu M. Drought hazard assessment and spatial characteristics analysis in China. Journal of Geographical Sciences. 2011; 21(2):235-49.
    15. Krause P, Boyle DP, Bäse F. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences. 2005; 5:89-97.
    16. Mishra AK, Singh VP. A review of drought concepts. Journal of Hydrology. 2010; 391(1):202-16.
    17. Nasiri M, Jabbari S, Boostani F, Shamsnia S. Analysis and monitoring of the drought by SPI. National Congress on water disaster. Marvdasht; 2009 [Persian]
    18. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology. 2004; 291(1):52-66.
    19. Nguyen LB, Li QF, Ngoc TA, Hiramatsu K. Adaptive Neuro–Fuzzy Inference System for Drought Forecasting in the Cai River Basin in Vietnam. Journal of the Faculty of Agriculture Kyushu University. 2015; 60(2): 405-415.
    20. Pirmoradian N, Shamsnia SA, Shahrokhnia MA. Monitoring and Spatial Analysis of Drought Severity 2000-2001Crop Year in Fars Province Using Standardized Precipitation Index in The Geographic Information Systems (GIS). Water Resources Engineering. 2009; 1(2): 65-74.
    21. Tsakiris G, Vangelis H. Towards a Drought Watch System based on Spatial SPI. Water Resources Management. 2004; 18(1): 1–12.
    22. Zargar A, Sadiq R, Naser B, Khan FI. A review of drought indices. Environmental Reviews. 2011; 19: 333-349.


Volume 3, Issue 2
June 2016
Pages 205-218
  • Receive Date: 23 July 2016
  • Revise Date: 10 November 2016
  • Accept Date: 17 September 2016
  • First Publish Date: 17 September 2016