Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea

Maher A. El-Hallaq; Mohammed O. Habboub

doi:10.4236/ars.2015.41008

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Advances in Remote Sensing > Vol.4 No.1, March 2015

Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea ()

Maher A. El-Hallaq¹, Mohammed O. Habboub²

¹Surveying and Geodesy, Civil Engineering Department, The Islamic University of Gaza, Gaza, Palestine.
²GIS, IT Department, The University College of Applied Science, Gaza, Palestine.

DOI: 10.4236/ars.2015.41008 PDF HTML XML 4,069 Downloads 5,055 Views Citations

Abstract

In order to make a rational prediction of the Dead Sea shape, data were prepared for suitability map creation using Markov Chain analysis and Multi Criteria Evaluation (MCE). Then, Markov Cellular Automata model and spatial statistics were used in prediction and validation processes. The validation process shows a standard Kappa index of 0.9545 which means a strong relation between the model and reality. The predicted shapes of years 2020, 2030 and 2040 follow the same conditions from 1984 to 2010. The predicted areas of 2020, 2030 and 2040 are 610, 591 and 574 km2 which are considered a logical extension of the trend from 1984 till 2010. This study can be used as an environmental alert in order to keep the Dead Sea alive. Moreover, Markov-Cellular Automata model can be used to predict closed seas as the Dead Sea from remote sensed data.

Keywords

Dead Sea, Markov Chain Analysis, Cellular Atutomata, Multi Criteria Evaluation

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El-Hallaq, M. and Habboub, M. (2015) Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea. Advances in Remote Sensing, 4, 83-95. doi: 10.4236/ars.2015.41008.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Eastman, J.R. (2012) IDRISI Selva Manual. IDRISI Tutorial. s.l. Clark University, Worcester. www.clarklabs.org
[2]	Tang, J., Wang, L. and Yao, Z. (2007) Spatio-Temporal Urban Landscape Change Analysis Using the Markov Chain Model and a Modified Genetic Algorithm. International Journal of Remote Sensing, 28, 3255-3271. http://dx.doi.org/10.1080/01431160600962749
[3]	Briassoulis, H. (2012) Analysis of Land Use Change: Theoretical and Modeling Approaches. Regional Research Institute, Thunder Bay. http://www.rri.wvu.edu/Webbook/Briassoulis/contents.htm
[4]	Memarian, H., Balasundram, S.K., Talib, J.B., Sung, C.T.B., Sood, A.M. and Abbaspour, K. (2012) Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia. Journal of Geographic Information System, 4, 542-554. http://dx.doi.org/10.1080/01431160600962749
[5]	Yao, X. (2010) Introduction to Natural Computation: Cellular-Automata. Lecture Notes, University of Birmingham, Birmingham. http://www.cs.bham.ac.uk/internal/courses/intro-nc/current/notes/02-cellular-automata.pdf
[6]	Samat, N., Hasni, R., Elhadary, Y. and Eltayeb, A. (2011) Modelling Land Use Changes at the Peri-Urban Areas Using Geographic Information Systems and Cellular Automata Model. Journal of Sustainable Development, 4, 72-84. http://dx.doi.org/10.1080/01431160600962749
[7]	Zhang, Q. (2009) Spatial-Temporal Patterns of Urban Growth in Shanghai, China: Monitoring, Analysis, and Simulation. Licentiate Thesis, Royal Institute of Technology (KTH), Stockholm.
[8]	Wen, W. (2008) Wetland Change Prediction Using Markov Cellular Automata Model in Lore Lindu National Park Central Sulawesi Province, Master Thesis. BOGOR Agricultural University, Bogor.
[9]	Cabral, P. and Zamyatin, A. (2006) Three Land Change Models for Urban Dynamics Analysis in Sintra-Cascais Area. 1st EARSeL Workshop of the SIG Urban Remote Sensing, Berlin, 2-3 March 2006, 1-8.
[10]	Kushwaha, S.P.S., Nandy, S., Ahmad, M. and Agarwal, R. (2014) Forest Ecosystem Dynamics Assessment and Predictive Modelling in Eastern Himalaya. Elsevier Ecological Indicators Journal, 45, 444-455.
[11]	Pontius, R.G. (2000) Quantification Error versus Location Error in Comparison of Catagorical Maps. Photogrammetric Engineering & Remote Sensing, 66, 1011-1016.
[12]	Pontius, R.G. (2002) Statistical Methods to Partition Effects of Quantity and Location during Comparison of Categorical Maps at Multiple Resolutions. Photogrammetric Engineering & Remote Sensing, 68, 1041-1049.
[13]	Pontius, R.G. and Millones, M. (2011) Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment. International Journal of Remote Sensing, 32, 4407-4429. http://dx.doi.org/10.1080/01431161.2011.552923
[14]	Morin, E. (2009) Flash Flood Prediction in the Dead Sea Region Utilizing Radar Rainfall Data. Journal of Dead-Sea and Arava Research, 1, 14-24.
[15]	U.S. Geological Survey (1998) Overview of Middle East Water Resources. Executive Action Team, 41.
[16]	El-Hallaq, M.A. and Habboub, M.O. (2014) Using GIS for Time Series Analysis of the Dead Sea from Remotely Sensing Data. Open Journal of Civil Engineering, 4, 386-396. http://dx.doi.org/10.4236/ojce.2014.44033
[17]	Green, E.P., Mumby, P.J., Edwards, A.J. and Clark, C.D. (2000) Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris.