IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p15625-d982783.html
   My bibliography  Save this article

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Author

Listed:
  • Milad Asadi

    (Department of Urban Planning, Daneshpajoohan Pishro Higher Education Institute, Isfahan 81747-47144, Iran)

  • Amir Oshnooei-Nooshabadi

    (Department of Geography, Lorestan University, Khorramabad 68151-44316, Iran)

  • Samira-Sadat Saleh

    (College of Science, North Texas University, Denton, TX 76203, USA)

  • Fattaneh Habibnezhad

    (Department of Geography and Urban Planning, Kharazmi University, Tehran 31979-37551, Iran)

  • Sonia Sarafraz-Asbagh

    (Department of Geography and Urban Planning, University of Tabriz, Tabriz 51666-16471, Iran)

  • John Lodewijk Van Genderen

    (International Institute for Geo-Information Science and Earth Observation (ITC), 7500 Enschede, The Netherlands)

Abstract

Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Suggested Citation

  • Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15625-:d:982783
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/15625/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/15625/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mansour, Shawky & Al-Belushi, Mohammed & Al-Awadhi, Talal, 2020. "Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques," Land Use Policy, Elsevier, vol. 91(C).
    2. Siqi Yi & Yong Zhou & Qing Li, 2022. "A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model," Land, MDPI, vol. 11(3), pages 1-16, March.
    3. de Almeida, Arlete Silva & Vieira, Ima Célia Guimarães & Ferraz, Silvio F.B., 2020. "Long-term assessment of oil palm expansion and landscape change in the eastern Brazilian Amazon," Land Use Policy, Elsevier, vol. 90(C).
    4. Dipanwita Dutta & Atiqur Rahman & S. K. Paul & Arnab Kundu, 2020. "Estimating urban growth in peri-urban areas and its interrelationships with built-up density using earth observation datasets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(1), pages 67-82, August.
    5. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    6. Chen, Yanguang & Huang, Linshan, 2019. "Modeling growth curve of fractal dimension of urban form of Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1038-1056.
    7. Jan Bebbington, 2001. "Sustainable development: a review of the international development, business and accounting literature," Accounting Forum, Taylor & Francis Journals, vol. 25(2), pages 128-157, June.
    8. Yusuf Alizade Govarchin Ghale & Abdusselam Altunkaynak & Alper Unal, 2018. "Investigation Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using Water Budget and Drought Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 325-337, January.
    9. Fernando Rubiera-Morollón & Ruben Garrido-Yserte, 2020. "Recent Literature about Urban Sprawl: A Renewed Relevance of the Phenomenon from the Perspective of Environmental Sustainability," Sustainability, MDPI, vol. 12(16), pages 1-14, August.
    10. Daniel Coq-Huelva & Rosario Asián-Chaves, 2019. "Urban Sprawl and Sustainable Urban Policies. A Review of the Cases of Lima, Mexico City and Santiago de Chile," Sustainability, MDPI, vol. 11(20), pages 1-22, October.
    11. Rahel Hamad & Heiko Balzter & Kamal Kolo, 2018. "Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    12. Biswajit Nath & Zheng Niu & Ramesh P. Singh, 2018. "Land Use and Land Cover Changes, and Environment and Risk Evaluation of Dujiangyan City (SW China) Using Remote Sensing and GIS Techniques," Sustainability, MDPI, vol. 10(12), pages 1-32, December.
    13. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    14. Muhammad Fahad Baqa & Fang Chen & Linlin Lu & Salman Qureshi & Aqil Tariq & Siyuan Wang & Linhai Jing & Salma Hamza & Qingting Li, 2021. "Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan," Land, MDPI, vol. 10(7), pages 1-17, July.
    15. Vernon Henderson, 2002. "Urbanization in Developing Countries," The World Bank Research Observer, World Bank, vol. 17(1), pages 89-112.
    16. M. Chitsazan & N. Aghazadeh & Y. Mirzaee & Y. Golestan, 2019. "Hydrochemical characteristics and the impact of anthropogenic activity on groundwater quality in suburban area of Urmia city, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(1), pages 331-351, February.
    17. Erqi Xu & Yimeng Chen, 2019. "Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology," Sustainability, MDPI, vol. 11(18), pages 1-24, September.
    18. Muhammad Hadi Saputra & Han Soo Lee, 2019. "Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton," Sustainability, MDPI, vol. 11(11), pages 1-16, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Liu & Chunchun Hu & Xionghua Kang & Fei Chen, 2023. "A Loosely Coupled Model for Simulating and Predicting Land Use Changes," Land, MDPI, vol. 12(1), pages 1-19, January.
    2. Qihao Wang & Dongya Liu & Feiyao Gao & Xinqi Zheng & Yiqun Shang, 2023. "A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model," Land, MDPI, vol. 12(2), pages 1-20, February.
    3. Wafaa Majeed Mutashar Al-Hameedi & Jie Chen & Cheechouyang Faichia & Biswajit Nath & Bazel Al-Shaibah & Ali Al-Aizari, 2022. "Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models," Sustainability, MDPI, vol. 14(14), pages 1-31, July.
    4. Changqing Sun & Yulong Bao & Battsengel Vandansambuu & Yuhai Bao, 2022. "Simulation and Prediction of Land Use/Cover Changes Based on CLUE-S and CA-Markov Models: A Case Study of a Typical Pastoral Area in Mongolia," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    5. Luoman Pu & Jiuchun Yang & Lingxue Yu & Changsheng Xiong & Fengqin Yan & Yubo Zhang & Shuwen Zhang, 2021. "Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050," IJERPH, MDPI, vol. 18(3), pages 1-21, January.
    6. Sajjad Hussain & Linlin Lu & Muhammad Mubeen & Wajid Nasim & Shankar Karuppannan & Shah Fahad & Aqil Tariq & B. G. Mousa & Faisal Mumtaz & Muhammad Aslam, 2022. "Spatiotemporal Variation in Land Use Land Cover in the Response to Local Climate Change Using Multispectral Remote Sensing Data," Land, MDPI, vol. 11(5), pages 1-19, April.
    7. Mirza Waleed & Muhammad Sajjad & Anthony Owusu Acheampong & Md. Tauhidul Alam, 2023. "Towards Sustainable and Livable Cities: Leveraging Remote Sensing, Machine Learning, and Geo-Information Modelling to Explore and Predict Thermal Field Variance in Response to Urban Growth," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    8. Liang Lv & Shihao Zhang & Jie Zhu & Ziming Wang & Zhe Wang & Guoqing Li & Chen Yang, 2022. "Ecological Restoration Strategies for Mountainous Cities Based on Ecological Security Patterns and Circuit Theory: A Case of Central Urban Areas in Chongqing, China," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    9. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    10. Yangcheng Hu & Yi Liu & Changyan Li, 2022. "Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value in the Middle Reaches of Yangtze River Urban Agglomeration," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    11. Jinling Zhang & Ying Hou & Yifan Dong & Cun Wang & Weiping Chen, 2022. "Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
    12. Sidney Turner & Richard Turner, 2011. "Capital cities: a special case in urban development," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(1), pages 19-35, February.
    13. Sri Murniani Angelina Letsoin & David Herak & Fajar Rahmawan & Ratna Chrismiari Purwestri, 2020. "Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    14. Qing Liu & Dongdong Yang & Lei Cao & Bruce Anderson, 2022. "Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China," Land, MDPI, vol. 11(2), pages 1-24, February.
    15. Ge Wang & Ziqi Zhou & Jianguo Xia & Dinghua Ou & Jianbo Fei & Shunya Gong & Yuxiao Xiang, 2023. "Optimal Allocation of Territorial Space in the Minjiang River Basin Based on a Double Optimization Simulation Model," Land, MDPI, vol. 12(11), pages 1-26, October.
    16. Zhang, Zuo & Li, Jiaming, 2022. "Spatial suitability and multi-scenarios for land use: Simulation and policy insights from the production-living-ecological perspective," Land Use Policy, Elsevier, vol. 119(C).
    17. Bhattarai, Keshav & Adhikari, Ambika P. & Gautam, Shiva, 2023. "State of Urbanization in Nepal: The Official Definition and Reality," SocArXiv gbwvk, Center for Open Science.
    18. Anthony Goerzen & Michael Sartor & Kristin Brandl & Stacey Fitzsimmons, 2023. "Widening the lens: Multilevel drivers of firm corporate social performance," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 54(1), pages 42-60, February.
    19. Diao, Xinshen & Magalhaes, Eduardo & Silver, Jed, 2019. "Cities and rural transformation: A spatial analysis of rural livelihoods in Ghana," World Development, Elsevier, vol. 121(C), pages 141-157.
    20. Abdulaziz I. Almulhim & Simon Elias Bibri & Ayyoob Sharifi & Shakil Ahmad & Khalid Mohammed Almatar, 2022. "Emerging Trends and Knowledge Structures of Urbanization and Environmental Sustainability: A Regional Perspective," Sustainability, MDPI, vol. 14(20), pages 1-23, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15625-:d:982783. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.