Skip to main content

Advertisement

Springer Nature Link
Log in

Analysis and Prediction of Urban Growth Using Neural-Network-Coupled Agent-Based Cellular Automata Model for Chennai Metropolitan Area, Tamil Nadu, India

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Chennai is one of the most densely populated cities in India facing challenges in shifting the city to metropolitan or mega city in the last two decades with continuing agglomeration. To model the growth of Chennai city, we have used cellular automata-based urban growth models based on the historical datasets. In the present study, urban growth of Chennai Metropolitan Area (CMA) was predicted for the year 2017 based on 2010 and 2013 dataset and Chennai city master plan using neural-network-coupled agent-based cellular automata (NNACA) model. Eight different agents of urbanization including transportation, hotspots, and industries were used in the prediction modeling. On validating the 2017 predicted outputs, NNACA model with hotspots proved to be better (hits: 498.52 km2) than that of without hotspots (hits: 488.31 km2). Out of the total eight agents of urbanization, the most influencing agent of urbanization of 2017 was identified to be the neighborhood of ‘Existing built-up of 2013’ using ‘sensitivity analysis’. Further, the urban sprawl of CMA for 2010, 2013 and 2017 was measured through Shannon’s entropy. The study area was divided into five directional and distance-based zones with the State Secretariat as the center. Entropy values suggest the need for more careful planning for further development in the southern region of CMA which has undergone congested urban growth while urbanization is dispersed in the northern part of the study region which can be thought for future urban developments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Aarthi, A. D., & Gnanappazham, L. (2018). Urban growth prediction using neural network coupled agents-based cellular automata model for Sriperumbudur Taluk, Tamil Nadu, India. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 353–362.

    Article  Google Scholar 

  • Ahmed, B., Ahmed, R., & Zhu, X. (2013). Evaluation of model validation techniques in land cover dynamics. ISPRS International Journal of Geo-Information, 2(3), 577–597.

    Article  Google Scholar 

  • Al-Ahmadi, K., See, L., & Heppenstall, A. (2013). Validating spatial patterns of urban growth from a cellular automata model. In A. Salcido (Ed.), Emerging applications of cellular automata (p. 26). London: InTechOpen.

    Google Scholar 

  • Aqbelaghi, A. S., Ghorbani, M., Farhadi, E., & Shafiee, H. (2018). Environmental approach in modelling of urban growth: Tehran City, Iran. Asian Journal of Water, Environment and Pollution, 15(2), 47–56.

    Article  Google Scholar 

  • Basse, R. M., Omrani, H., Charif, O., Gerber, P., & Bódis, K. (2014). Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171.

    Article  Google Scholar 

  • Batty, M. (2005). Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. Cambridge, MA: MIT Press.

    Google Scholar 

  • Batty, M., & Xie, Y. (1994). From cells to cities. Environment and Planning B: Urban Analytics and City Science, 21(7), S31–S48.

    Article  Google Scholar 

  • Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233.

    Article  Google Scholar 

  • Bishop, C. M. (1995). Neural networks for pattern recognition. New York: Oxford University Press.

    Google Scholar 

  • Chennai District Statistical Hand Book. (2017). Department of Economics and Statistics, Government of Tamil Nadu. https://cdn.s3waas.gov.in/s313f3cf8c531952d72e5847c4183e6910/uploads/2018/06/2018062923.pdf. Accessed 31 March 2019.

  • Eastman, J. R. (2012). IDRISI selva tutorial, manual version 17. http://uhulag.mendelu.cz/files/pagesdata/eng/gis/idrisi_selva_tutorial.pdf. Accessed 31 March 2019.

  • Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421.

    Article  Google Scholar 

  • He, J., Li, X., Yao, Y., Hong, Y., & Jinbao, Z. (2018). Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science, 32(10), 2076–2097.

    Article  Google Scholar 

  • Hill, A., & Lindner, C. (2010) Modelling informal urban growth under rapid urbanisation—A CA-based land-use simulation model for the city of Dar es Salaam, Tanzania. Ph.D. thesis (p. 46). Dortmund: Technical University of Dortmund.

  • Housing and Urban Development Department, Chennai Metropolitan Area. (2018). http://www.cmdachennai.gov.in/pdfs/go/2018/go13.pdf. Accessed 31 March 2019.

  • Hua, L., Tang, L., Cui, S., & Yin, K. (2014). Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability, 6(6), 3899–3914.

    Article  Google Scholar 

  • Jokar Arsanjani, J., Helbich, M., & de Noronha Vaz, E. (2013). Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran. Cities, 32, 33–42.

    Article  Google Scholar 

  • Liu, Y. (2009). Modelling urban development with geographical information systems and cellular automata. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Ministry of Environment, Forest and Climate Change. (2016). Government of India. http://pib.nic.in/newsite/PrintRelease.aspx?relid=137373. Accessed 31 March 2019.

  • Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116.

    Article  Google Scholar 

  • Mubea, K. W. (2014). Scenarios of urban growth in Kenya using regionalized cellular automata based on multi temporal landsat satellite data. Ph.D. thesis (p. 3). Bonn: University of Bonn.

  • Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108–122.

    Article  Google Scholar 

  • Second Master Plan for Chennai Metropolitan Area. (2008a). http://www.cmdachennai.gov.in/Volume1_English_PDF/Vol1_Chapter03_Economy.pdf. Accessed 31 March 2019.

  • Second Master Plan for Chennai Metropolitan Area. (2008b). http://www.cmdachennai.gov.in/Volume2_English_PDF/DR-English.pdf. Accessed 31 March 2019.

  • Second Master Plan for Chennai Metropolitan Area. (2008c). http://www.cmdachennai.gov.in/Volume3_English_PDF/Vol3_Chapter03_Demography.pdf. Accessed 31 March 2019.

  • Sekar, S. P., & Kanchanamala, S. (2011). An analysis of growth dynamics in Chennai Metropolitan Area. Institute of Town Planners, 8(4), 31–57.

    Google Scholar 

  • Serasinghe Pathiranage, I. S., Kantakumar, L. N., & Sundaramoorthy, S. (2018). Remote sensing data and SLEUTH urban growth model: As decision support tools for urban planning. Chinese Geographical Science, 28(2), 274–286.

    Article  Google Scholar 

  • Shafizadeh-Moghadam, H., Asghari, A., Tayyebi, A., & Taleai, M. (2017). Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Computers, Environment and Urban Systems, 64, 297–308.

    Article  Google Scholar 

  • Sukawattanavijit, C., Chen, J., & Zhang, H. (2017). GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(3), 284–288.

    Article  Google Scholar 

  • Triantakonstantis, D., & Mountrakis, G. (2012). Urban growth prediction: A review of computational models and human perceptions. Journal of Geographic Information System, 4, 555–587.

    Article  Google Scholar 

  • Wolfram, S. (1984). Cellular automata as models of complexity. Nature, 311(5985), 419–424.

    Article  Google Scholar 

  • Yang, X., Zhao, Y., Chen, R., & Zheng, X. (2016). Simulating land use change by integrating landscape metrics into ANN-CA in a new way. Frontiers of Earth Science, 10(2), 245–252.

    Article  Google Scholar 

  • Yeh, A. G.-O., & Li, X. (2002). Urban simulation using neural networks and cellular automata for land use planning. In D. E. Richardson & P. van Oosterom (Eds.), Advances in spatial data handling (pp. 451–464). Berlin: Springer.

    Chapter  Google Scholar 

  • Zhang, X. (2016). Urban growth modeling using neural network simulation: A case study of Dongguan City, China. Journal of Geographic Information System, 8(3), 317–328.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Trivandrum, India

    Aarthi Aishwarya Devendran & Gnanappazham Lakshmanan

Authors
  1. Aarthi Aishwarya Devendran
    View author publications

    Search author on:PubMed Google Scholar

  2. Gnanappazham Lakshmanan
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Gnanappazham Lakshmanan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devendran, A.A., Lakshmanan, G. Analysis and Prediction of Urban Growth Using Neural-Network-Coupled Agent-Based Cellular Automata Model for Chennai Metropolitan Area, Tamil Nadu, India. J Indian Soc Remote Sens 47, 1515–1526 (2019). https://doi.org/10.1007/s12524-019-01003-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-019-01003-8

Keywords

Profiles

  1. Aarthi Aishwarya Devendran View author profile