The paper deals with the validation and evaluation of mathematical models in
natural hazard analysis, with a special focus on establishing their predictive power.
Although most of the tools and statistics available are common to general classification
models, some peculiarites arise in the case of hazard assessment. This is due to the fact
that the target for validation, the propensity to develop a dangerous characteristic, is not
really known and must be estimated from a (usually) very small sample. This implies
that the two types of errors (false positives and false negatives) should be given
different meanings. Related to this, a very frequent situation is the presence of
prevalence (different proportion of positive and negative cases) in the sample. It is
shown that sample prevalence can have a dramatic effect in some very common
validation statistics, like the confusion matrix and model efficiency. Here some statistics
based on the confusion matrix are presented and discussed, and the use of thresholdindependent
approaches (especially the ROC plot) is shown. The ROC plot is also
proposed as a convenient tool for decision-taking in a risk management context. A
general scheme for hazard predictive modeling is finally proposed
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