Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management

By S. Beguerı́a. 2006

479 citations recorded by the source corpus.

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

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In Environmental Engineering

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Citations in the corpus

Statistical and spatial analysis of landslide susceptibility maps with different classification systems (2016, methodology): “To compute this index, the error matrix (Begueria 2006) was prepared for each pair of LSMs.”
Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting (2015, background): “The results of the four confusion matrixes (true positives, true negatives, false positives and false negatives) are combined to define some indexes commonly used to evaluate model performances in hazard assessment (Begueria, 2005) and in rainfall thresholds (Martelloni et al.”
Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold (2012, background): “Beguería (2006) has shown that prevalence raises the need to use threshold independent statistics such as the receiver operating characteristics (ROC) curve for the validation of predictive models.”
Landslide susceptibility modelling using different advanced decision trees methods (2018, methodology): “The ROC curve is a popular validation method of landslide models (Beguería 2006).”
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (2010, methodology): “one of the most common used accuracy statistics for the prediction models in natural hazard assessments (Begueria, 2006).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, background): “2007) or risk assessment (Beguerı́a 2006) – to (very) a high number (Zaniewski et al.”
Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows (2014, unknown): “…stepwise procedure used here and inBrenning(2005) differs from other studies where the decision of keeping or dropping predictor variables is based on the significance of model improvement (e.g.Beguería, 2006; Meusburger and Alewell, 2009; Guns and Vanacker, 2012), not on an information criterion.”