Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin
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DOI: 10.1016/j.geomorph.2019.106975
Abstract: Abstract The quality of “non-landslide” negative samples may result in unreasonable prediction results for machine learning (ML) models. The aim of this study is to improve the performance of ML models by perfecting the quality… read more here.
Keywords: machine learning; susceptibility; jinsha river; area ... See more keywords
Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province
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DOI: 10.1080/19479832.2021.1961316
Abstract: ABSTRACT The quality of “non-landslide’ samples data impacts the accuracy of geological hazard risk assessment. This research proposed a method to improve the performance of support vector machine (SVM) by perfecting the quality of ‘non-landslide’… read more here.
Keywords: scenario; based fcm; model; landslide susceptibility ... See more keywords
Non-decreasable extremal Beltrami differentials of non-landslide type
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DOI: 10.1142/s0129167x18500660
Abstract: In this paper, we deform a uniquely-extremal Beltrami differential into different non-decreasable Beltrami differentials, and then construct non-unique extremal Beltrami differentials such that they are both non-landslide and non-decreasable. read more here.
Keywords: extremal beltrami; differentials non; beltrami differentials; non decreasable ... See more keywords