Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. Brenning, A. 5(6):853–862. ![Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting "present" and "future" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.
@article{brenningSpatialPredictionModels2005,
title = {Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation},
author = {Brenning, A.},
date = {2005-11},
journaltitle = {Natural Hazards and Earth System Science},
volume = {5},
pages = {853--862},
issn = {1561-8633},
doi = {10.5194/nhess-5-853-2005},
url = {https://doi.org/10.5194/nhess-5-853-2005},
abstract = {The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting "present" and "future" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12317042,~to-add-doi-URL,classification-trees,environmental-modelling,landslides,logistic-regression,modelling,natural-hazards,overfitting,regression,review,support-vector-machines},
number = {6}
}
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