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基于知识图谱的滑坡易发性评价文献综述及研究进展
郭飞, 赖鹏, 黄发明, 刘磊磊, 王秀娟, 何政宇
PDF(2501 KB)
PDF(2501 KB)
基于知识图谱的滑坡易发性评价文献综述及研究进展
Literature Review and Research Progress of Landslide Susceptibility Mapping Based on Knowledge Graph
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滑坡易发性评价是滑坡风险评估的基础和核心内容，开展滑坡易发性文献计量分析可以定量化地分析其研究进展及发展趋势，为国内开展地灾风险评估工作提供参考.利用Web of Science和CNKI数据库，基于CiteSpace可视化知识图谱分析工具对1985-2022年已发表的滑坡易发性文献进行计量分析，并对摘要部分进行了LDA分析，来细分该领域内的研究.结果表明：（1）滑坡易发性评价仍然是当前的研究热点，中国是滑坡易发性研究较为活跃的国家且国际间合作较多；（2）滑坡易发性领域发文量前10的作者中4位来自中国，中国科学院成为发文最多的机构，接收易发性评价类文章最多的中英文期刊分别是《中国地质灾害与防治学报》和《Natural Hazard》，中国国家自然科学基金和国土资源大调查项目大力资助了滑坡易发性课题的研究；（3）近5年来，机器学习模型（包括深度学习等）在滑坡易发性的应用快速增长，已成为最热门的研究方法；（4）为了实现滑坡易发性建模的精简化和智能化，并进一步提高滑坡易发性评价结果的精度和实用性，滑坡易发性在滑坡编目、指标体系、评价单元、评价模型、联接方法和精度评价等方面还需开展深入探索.
Landslide susceptibility mapping (LSM) is the foundation and critical part of landslide risk assessment. The bibliometric analysis of LSM literature can be applied to quantitatively analyze the research progress and development trend. The result will provide references for geological hazard risk assessment in China. In this study, based on the Web of Science and CNKI databases, the CiteSpace visual knowledge graph analysis tool has been used to carry out bibliometric analysis of LSM literature from 1985 to 2022. Moreover, the LDA analysis has been conducted on the abstract to subdivide the research in this field. The results show that: (1) LSM is still a research hotspot at present. In China, there are a large number of studies and international cooperation about LSM. (2) Four of the top 10 authors in the number of published papers on LSM are from China. The institution that has published the most papers on LSM is the Chinese Academy of Sciences. The Chinese Journal of Geological Hazard and Control is the most popular Chinese journal and the Natural Hazardsis the most popular English journals to publish LSM papers. The research on the subject of LSM has been substantially funded by the National Natural Science Foundation of China and the National Land and Resources Survey Project. (3) In the past five years, machine learning models (including deep learning, etc.) have been widely used as the most popular LSM models. (4) In order to achieve the simplification and intelligence of landslide susceptibility modeling and to improve the accuracy and practicability of the LSM results, the following parts of LSM, including the landslide inventory, conditioning factors, assessment unit, assessment model, connection methods and accuracy verification, need to be deeply explored in further studies.
滑坡易发性 / CiteSpace / 知识图谱 / 计量分析 / LDA主题模型 / 灾害 / 工程地质
landslide susceptibility / CiteSpace / knowledge graph / bibliometric analysis / Latent Dirichlet Allocation model / hazards / engineering geology
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