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Evolutionary Overview and Prediction of Themes in the Field of Land Degradation

Author

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  • Xinhai Lu

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
    College of Public Administration, Central China Normal University, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Yanwei Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
    These authors contributed equally to this work.)

  • Chaoran Lin

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Feng Wu

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Land degradation has become one of the major global environmental problems threatening human well-being. Whether degraded land can be restored has a profound effect on the achievement of the 2030 UN Sustainable Development Goals. Therefore, the ways by which to identify the current research status and potential research topics in the massive scientific literature data in the field of land degradation is a crucial issue for scientific research institutions in various countries. In view of the shortcomings in the current research on the thematic evolution and thematic and thematic prediction, such as the ignorance of random features during scientific innovation, the defects of manual classification, and the difficulty of identifying technical terms, this research proposes a new combined method. First, the Latent Dirichlet Allocation (LDA) algorithm in machine learning is used to capture the potential clustering of themes in the literature sample set of land degradation research. The distribution characteristics and evolution of themes in each period are then analyzed. The method is combined with the Hidden Markov Model (HMM), which contains double stochastic process to quantitatively predict the trend of future thematic evolution. Finally, the above-mentioned combined method is used to analyze the evolution characteristics and future development trends of the themes in the field of land degradation. Comparative experiments show that the method in this study is effective and practical. The research results show that rangeland degradation, surface temperature, island, soil degradation, water quality, crop productivity and restoration are important research topics in the field of land degradation in the future. In addition, based on the advantages of this model, this model can be widely used in the thematic evolution and prediction analysis of different research fields in land use science.

Suggested Citation

  • Xinhai Lu & Yanwei Zhang & Chaoran Lin & Feng Wu, 2021. "Evolutionary Overview and Prediction of Themes in the Field of Land Degradation," Land, MDPI, vol. 10(3), pages 1-23, March.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:3:p:241-:d:508052
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    References listed on IDEAS

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    3. Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.

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