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Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Author

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  • Shufang Wang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Xiyun Jiao

    (College of Agricultural Engineering, Hohai University, Nanjing 210098, China
    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China)

  • Liping Wang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Aimin Gong

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Honghui Sang

    (College of Agricultural Engineering, Hohai University, Nanjing 210098, China)

  • Mohamed Khaled Salahou

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China)

  • Liudong Zhang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

Abstract

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

Suggested Citation

  • Shufang Wang & Xiyun Jiao & Liping Wang & Aimin Gong & Honghui Sang & Mohamed Khaled Salahou & Liudong Zhang, 2020. "Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis," Sustainability, MDPI, vol. 12(4), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1396-:d:320362
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    References listed on IDEAS

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    1. Guan, DongJie & Li, HaiFeng & Inohae, Takuro & Su, Weici & Nagaie, Tadashi & Hokao, Kazunori, 2011. "Modeling urban land use change by the integration of cellular automaton and Markov model," Ecological Modelling, Elsevier, vol. 222(20), pages 3761-3772.
    2. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    3. Kritsana Kityuttachai & Nitin Kumar Tripathi & Taravudh Tipdecho & Rajendra Shrestha, 2013. "CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand," Sustainability, MDPI, vol. 5(4), pages 1-21, April.
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    Cited by:

    1. Wang, Quan & Wang, Haijun & Chang, Ruihan & Zeng, Haoran & Bai, Xuepiao, 2022. "Dynamic simulation patterns and spatiotemporal analysis of land-use/land-cover changes in the Wuhan metropolitan area, China," Ecological Modelling, Elsevier, vol. 464(C).
    2. Can Kara & Naciye Doratlı, 2021. "Predict and Simulate Sustainable Urban Growth by Using GIS and MCE Based CA. Case of Famagusta in Northern Cyprus," Sustainability, MDPI, vol. 13(8), pages 1-27, April.

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