Simulation and prediction of drought using Cellular Automata and Markov methods in Najaf Abad plain

Document Type : Research Article

Authors

1 Graduate Student of RS and GIS, Faculty of Environment and Energy, Islamic Azad University, Science and Research Branch, Tehran

2 Professor, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran

3 Professor, Geography and Humanities, Isfahan University, Isfahan

Abstract

The governing factors of drought are non-linearly correlated. Therefore, researcher needs to apply nonlinear methods such as CA to model and predict the drought. CA and its derivatives are among novel methods of drought simulation that rarely used for predicting the drought. While such methods have simple structures, they provide high visual capabilities for drought monitoring. This paper investigates drought in Najaf Abad plain using Markov, CA Markov and Landsat satellite images. First, satellite image time series of transpiration were classified for 1995, 2008 and 2015, and the land zonation of drought condition was estimated. Then, the drought in 2020 was predicted using CA Markov. The Kappa index is 0.63 and the agreement between actual and predicted map (M (m)) is 0.85. Our findings showed that our proposed model can suitably predict the drought. In addition, the drought distribution map showing the possibility of changes in 2020, suggests that if the situation continues and no changes in the type of cultivation and cropping pattern happen, all areas in danger of drought in 2015, will face drought more intensely and more widely, in 2020.

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Main Subjects

References

 
[1]. Askarizade M, Behniafar A, Zabol Abasi F, Malbosi Sh. Regionalization of drought with index percentage of normal (PN) and deciles (DC) in Khorasan Razavi Province. Journal of human settlements planning. 2015;3(7):27. [Persian]
 
[2]. Fathi merj A, Heidarian A. Evaluation of meteorological drought, agricultural and hydrological using GIS in Khozestan province. Iran-Watershed Management Science & Engineering. 2010;7(23):19-32. [Persian]
[3]. Samadi H, Ebrahimi A. Drought and surface water and groundwater resources. Drought Impacts and its Mitigation Approaches. First ed. Sahrekord University: Soroosh; 2010.p. 115. [Persian]
 [4]. Niknam H, Ajdari moghadam M, Khosravi M. Neuro-fuzzy model patterns and telecommunications to predict drought Case Study: Zahedan. 4th international congress of the Islamic World Geographers. Zahedan. 2010. [Persian]
[5]. Reza zade R, Mir Ahmadi M. Cellular Automation is a new method for simulation of urban growth. Journal of Technology of Education. 2008;4(6):47-55. [Persian]
 
[6]. Alam J, Rahman M, Sadaat A. Monitoring Meteorological and Agricultural Drought dynamics in Barind Region Bangladesh using Standard Precipitation Index and Markov Chain Model. Journal of Geomatics and Geosciences. 2013;3(3):511-524.
 
[7]. Avilés A, Célleri R, Solera A, Paredes J. Probabilistic Forecasting of Drought events using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin. Journal of water. 2016;8(37): 1.
 
[8]. Edalat Gostar M, Farzadian M, Amiri N. Stochastic models for prediction of drought in the county of Shiraz. National Conference on management of Water Crisis. Marvdasht. 2008. [Persian]
[9]. Hasan Zade E, Abdi Kordani A, Fakheri fard A. Prediction of drought using genetic algorithms and neural network model. Journal of water and wastewater. 2009;23(3):48-59. [Persian]
[10]. Rostami A, Razmkhah H, Fatahi M. Monitoring and development of artificial neural network model to predict drought using SPI index case study: Kohgiluyeh and Boyer. Faculty of Agriculture, Islamic Azad University of Shiraz. 2011:1. [Persian]
 
[11]. Darzi F, Safavi H, Mamanposh A. Modeling of return flow from Nekooabad network to Najaf Abad basin. Second Conference on Water Resources of Iran. Isfahan. 2006. [Persian]
[12]. Phedge N, Muralikrishna I V, Chalapatirao K V. Study of cellular Automata Models for urban growth. www. GIS Development.net.
 
[13]. OSullivan D. Exploring spatial process dynamics using irregular Cellular Automaton models. Journal of Geographical Analysis. 2001;33(1):1-18.
 
[14]. White R, Engelen G. High resolution integrated modeling of the spatial Dynamics of urban and regional systems, Computers, environment and urban System. 2000;24:383-400.
 
[15]. OSullivan D, Torrens P. Cellular models of urban systems. In: Bandini S, Worsch T, editor. Theory and Practical Issues on Cellular Automata. First ed. London: Springer; 2001.p. 108-116.
 
 
[16]. Ahadnejad M, Rabet A. Evaluation and forecast of Haman impacts based on land use changes using multi temporal satellite imagery and GIS: a case study on Zanjan. Journal of the Indian Society of Remote Sensing. 2009;37(4):659–669.
 
[17]. Hadavi F. Evaluation of physical development of the Zanjan city for optimized planning using GIS techniques. 1th National conference and exhibition Geomatics and conference of International Remote sensing. Tehran. 2011. [Persian]
Iranian journal of Ecohydrology
Volume 4, Issue 3
September 2017
Pages 653-662

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History

  • Receive Date: 30 November 2016
  • Revise Date: 20 April 2017
  • Accept Date: 22 April 2017
  • First Publish Date: 23 September 2017
  • Publish Date: 23 September 2017

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