How can statistical models help to determine driving factors of landslides

By P. Vorpahl, H. Elsenbeer, Michael Märker, B. Schröder. 2012

304 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/d2efec0f70ab1be10297b6b88c04e4f6b7c19ef3

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In Global and Planetary Change

In Management, Monitoring, Policy and Law

In Mechanical Engineering

Citations in the corpus

A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, methodology): “SPI is a parameter that is derived from the DEM and reflects the intensity and erosive power of slope surface run-off (Vorpahl et al. 2012; Jaafari et al. 2014).”
Improving species distribution models: the value of data on abundance (2014, methodology): “A perfectly calibrated model will have an intercept of zero and a slope of one (Reineking & Schr€oder 2006; Zurell et al. 2009; Vorpahl et al. 2012).”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, methodology): “A few researchers have utilized several quantitative techniques such as bivariate regression analysis [15,16], logistic regression [17–19], multivariate regression analysis [20,21], fuzzy logic [22], artificial neural network [11,23–25], decision tree [26,27], random forest [28], support vector mach”
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility (2017, background): “…fuzzy (Dehnavi et al., 2015; Pradhan, 2013), multivariate adaptive regression splines (MARS) (Conoscenti et al., 2015; Felicísimo et al., 2013; Vorpahl et al., 2012; Wang et al., 2015a), neural networks (Lee et al., 2007; Park et al., 2013; Tien Bui et al., 2016c; Yilmaz, 2010), fuzzy…”
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues (2013, methodology): “…is, in itself, quite innovative since it is the first time that such technique, widely used in remote sensing for image classification (Ham et al., 2005; Pal, 2005) but rarely adopted in landslide studies (Brenning, 2005; Vorpahl et al., 2012), is used for the production of a LSM over a large area.”
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, methodology): “Since default hyperparameter settings, which are used by some authors (Goetz et al., 2015; Ruß & Brenning, 2010; Ruß & Kruse, 2010; Vorpahl et al., 2012), can in no way guarantee an optimal performance of machine-learning techniques, additional attention should be directed to this potentially…”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, methodology): “In the case of BRT, it considers uncertainty reduction in order to achieve a final number of trees (Vorpahl et al., 2012).”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “…mining algorithms including boosted regression tree, classi ﬁ cation and regression tree, and random forest models (Miner et al. 2010; Stumpf and Kerle 2011; Vorpahl et al. 2012; Catani et al. 2013; Micheletti et al. 2014; Youssef 2015) have been implemented for landslide susceptibility modelling.”