Spatial prediction models for landslide hazards: review, comparison and evaluationReport as inadecuate




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1 Institute of Medical Informatics

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.





Author: A. Brenning -

Source: https://hal.archives-ouvertes.fr/



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