Spatiotemporal Monitoring and Prediction of Land Use Integrating the Markov Chain and Cellular Automata in the Coastal Chaouia
H. Souidi1, L. Ouadif2, L. Bahi3, N. Habitou4
1H.Souidi, 3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco.
2L.Ouadif, 3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco
3L. Bahi, 3GIE Laboratory, Mohammadia Engineering School, Mohammed V University in Rabat, Morocco.
4N. Habitou, Hydraulic Systems Analysis Team (HSAT), Mohammadia Engineering School, Mohammed V University in Rabat, Morocco.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1704-1711 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5760098319/2019©BEIESP | DOI: 10.35940/ijrte.C5760.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the last decades, the world population rate has been gradually increasing, this population growth has faced intense urban expansion and the rapid development of the agricultural and industrial sectors. This change had an impact on the mode of land use. In the face of this problem, several strategies have been created for monitoring and predicting possible future scenarios on rhythm of land use change. The CA-Markov model used in this research allows to predict future land use trends on the basis of the classified maps of 1987, 1999, 2011 and 2019. Simulating and tracking these maps is a major challenge. The latter provides important information in terms of data, methods and models to be used to create a realistic and sustainable process of territory planning for environmentalists, planners and local authorities. The combination of the Markov chain and cellular automata has been used to qualitatively and quantitatively simulate and evaluate future land use trends in coastal Chaouia, Morocco. To achieve this purpose, two maps were developed for the two years of 2027 and 2035. By using kappa, the global success of the modelling was 89.22% and 82.12% respectively in 2011 and 2019 for the projected land use map. The results confirm that forests have been affected by intensive agricultural uses. This increase in agricultural use is due to the impact of the constant increase in the development of the agroeconomic and demographic sectors. This situation indicates the need to create new approach to management to protect the sustainability of land use in coastal Chaouia.
Keywords: Coastal Chaouia , CA-Markov, Kappa, Markov Chain, Cellular Automat(CA).
Scope of the Article: Coastal Engineering.