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机器学习模型在滑坡易发性评价中的应用现状与发展趋势

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
滑坡易发性评价是滑坡危险性评价和风险性评价的必要基础,机器学习模型可以在海量滑坡灾害数据中挖掘潜在信息,建立数据联系,寻找滑坡现象背后的本质规律,是当前滑坡易发性评价研究的重要手段之一.基于文献检索平台,介绍不同机器学习模型等在滑坡易发性评价领域的应用现状,探讨了机器学习模型在滑坡易发性评价应用中的优缺点和适用情形,并提出以下几点认识:①基于耦合的机器学习模型在滑坡易发性评价中显著优于未耦合单一机器学习模型,基于改进的机器学习模型在滑坡易发性评价中也显著优于未经改进的单一机器学习模型;②探索模型算法"暗箱"式的作用机制、过程与结果的可解释性,提高滑坡易发性评价的精度和准确性,完善模型应用研究的不足,实现与人工智能的嵌套耦合等是未来机器学习模型研究的重要方向;③机器学习模型的自身特点和适用情形源于其构建原理,且作为固有特性贯穿于模型应用全过程.尽管机器学习模型受到广泛关注,但人们对其认识仍不够深入,学界也未形成一致的观点和统一的评价准则,尚有待进一步研究.
Application Status and Development Trend of Machine Learning models in Landslide Susceptibility Assessment
Landslide susceptibility evaluation is the necessary basis for landslide hazard evaluation and risk evaluation.Machine learning model can mine potential information in massive landslide disaster data,establish data connection,and find the essential law behind the land-slide phenomenon,which is one of the important means of current landslide susceptibility evaluation research.Based on the literature retrieval platform,this paper introduces the application status of different machine learning models in the field of landslide susceptibility evaluation,and discusses the advantages and disadvantages of machine learning models in the application of landslide susceptibility evaluation and the ap-plication situation,and puts forward the following understanding:①the coupled machine learning model is significantly better than the uncou-pled single machine learning model in landslide susceptibility evaluation,and the improved machine learning model is also significantly better than the unimproved single machine learning model in landslide susceptibility evaluation;②In the future,it is important to explore the mecha-nism of action,process and the interpretability of results of the"dark box"model algorithm,improve the accuracy and accuracy of landslide susceptibility evaluation,improve the shortcomings of model application research,and realize the nested coupling with artificial intelligence;③The characteristics and application of machine learning model are derived from its construction principle and run through the whole process of model application as inherent characteristics.Although machine learning model has received wide attention,people's understanding of it is still not deep enough.No consistent views and unified evaluation criteria have been formed yet,which needs further research.

machine learninglandslidegeological disasterslandslide susceptibility assessment

潘网生、蔚秀莲、赵所毅

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黔南民族师范学院 旅游与资源环境学院,贵州 都匀 558000

内蒙古自治区测绘地理信息中心,内蒙古呼和浩特 610072

机器学习 滑坡 地质灾害 滑坡易发性评价

黔南民族师范学院高层次引进人才研究专项项目贵州省教育厅创新群体重大研究项目黔南民族师范学院2024年度重点研究科研能力提升项目贵州省自然科学基金重点项目2022年度贵州省级"金课"地理信息系统国家级大学生创新训练项目黔南州"揭榜挂帅"教科研项目黔南民族师范学院教育质量提升工程项目

2021qnsyrc03黔教合KY字[2016]0542024zdzk07黔科合基础[2018]14162022JKHH03492023106700382024A0122022xjg026

2024

10.11907/rjdk.232143

软件导刊
湖北省信息学会

软件导刊

影响因子：0.524
ISSN：1672-7800
年,卷(期)：2024.23(7)