Journal of Applied Mathematics and Computation

ISSN Print: 2576-0645 Downloads: 89487 Total View: 1384478
Frequency: quarterly ISSN Online: 2576-0653 CODEN: JAMCEZ
Email: jamc@hillpublisher.com
Article 10.26855/ jamc.2018.05.002

Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Technique

Kamal Pandey1,*, Diptarshi Mitra2, Sandeep Maithani3, Harish Karnatak1
1GeoWeb Services, IT and Distance Learning, Indian Institute of Remote Sensing (IIRS), 4, Kalidas Road, Dehradun-248001
2Geoinformatics, IIRS, 4, Kalidas Road, Dehradun-248001
3Urban and Regional Studies, IIRS, 4, Kalidas Road, Dehradun-248001
*Corresponding author: Kamal Pandey, GeoWeb Services, IT and Distance Learning, Indian Institute of Remote Sensing (IIRS), 4, Kalidas Road, Dehradun-248001
E-mail: kamal@iirs.gov.in
Published: May 24,2018

Abstract

Nowadays, rapid urbanization is destabilizing the environment and the economy of a place. For undertaking sustainable urbanization (to protect the environmental balance), we first need to model and predict the urban growth in the near future. In this study, Cellular Automata – Markov (CA-Markov) model has been used to predict the urban growth of Dehradun (planning area) in 2035. For this, the classified images of Dehradun (showing urban and non-urban classes), for the years 1995 and 2015, have been taken as input. Along with these two images, three more classified images of Dehradun (showing urban and non-urban classes), pertaining to the years 2000, 2003 and 2010, have been used to draw a percentage urban area vs. year graph to find out the trend of urban growth. The percentage urban area for the year 2035 has been calculated both from the output obtained from the CA-Markov model (≈38%) and from this graph (≈32%). The values are more or less similar. Now, five factors influencing urban growth, viz., distance from the roads, distance from the rivers, distance from the residential areas, distance from the city centre and slope of the area, have been considered. Maps representing these parameters are created and weighted overlay is performed with them to find out the suitable areas of growth. The map obtained from weighted overlay shows the central part of Dehradun as the most suitable area for growth. This result agrees well with the classified images and the output from CA-Markov model.

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How to cite this paper

Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Technique

How to cite this paper: Pandey, K. et al.. (2018) Urban Growth Modelling Using Cellular Automata – Markov (CA-Markov) Technique. Journal of Applied Mathematics and Computation, 2(5), 178-187.
http://dx.doi.org/10.26855/ jamc.2018.05.002