Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

By Jason Goetz, Alexander Brenning, Helene Petschko, Philip Leopold. Computational Geosciences, 2015

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Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China (2018, methodology): “In the data-driven models, the machine learning models performed better, and are considered more 49 efficient than other approaches such as expert opinion based methods and analytic methods (Goetz et al., 2015; 50 Pham et al., 2016a).”
Applications of artificial intelligence for disaster management (2020, background): “2018a), LR (Goetz et al. 2015; Zhou et al. 2018a), random forest (RF) (Goetz et al.”
Space-time landslide predictive modelling (2019, background): “, 2014), and even in very different landscapes (Budimir et al., 2015; Goetz et al., 2015; Lombardo et al., 2016a; Reichenbach et al., 2018).”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, neutral): “an n ∗ n matrix [20].”
A comparative study of machine learning classifiers for modeling travel mode choice (2017, methodology): “…be applied to ar-230 bitrary prediction models, given that independent test data for evaluation purposes is available (e.g. Knudby231 et al., 2010; Xu et al., 2014; Goetz et al., 2015).232 In this research, a permutation-based approach to measure the overall importance of each variable is233 used.”