Review on the Application of Machine Learning Methods in Landslide Susceptibility Mapping

D. S. Mwakapesa, Ye Li, Xiangtai Wang, Binbin Guo, Mao Yimin
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Abstract

: Machine learning is a very important in computer science field which has gained attention in numerous applications. This paper reviewed various machine learning methods including supervised and unsupervised learning and highlighted their applications, advantages and disadvantages in landslide susceptibility mapping. The review has also mentioned the challenges of machine learning algorithms for achieving higher performance accuracy from the supervised and unsupervised learning algorithms during landslide susceptibility. Moreover, highlights on the application of deep learning methods as the current research in landslide susceptibility mapping has also been reported. Finally, this paper argued the necessity of thorough preparation of relevant and enough data being significant important to obtain high performance results from the review methods.
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机器学习方法在滑坡易感性制图中的应用综述
机器学习是计算机科学中一个非常重要的领域,在许多应用中得到了关注。本文综述了各种机器学习方法,包括监督学习和无监督学习,并重点介绍了它们在滑坡易感性制图中的应用和优缺点。该评论还提到了机器学习算法在滑坡易感性期间从监督和无监督学习算法中获得更高性能准确性的挑战。此外,还报道了深度学习方法在滑坡易感性制图中的应用。最后,本文认为充分准备相关和足够的数据对于从综述方法中获得高性能结果非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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