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A joint conditional probability model is proposed to represent a measure of a future landslide hazard, and five estimation procedures for the model are presented. The distribution of past landslides was divided into two groups with respect to a fixed time. A training set consisting of the earlier landslides and the geographical information system-based… (More)

Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so their degree of freedom is reduced. Thus, it is often unlikely to evaluate them based on traditional tests. Another reason, which is… (More)

This contribution considers the predictions of mass movements that have yet to take place and the support provided by spatial databases for the prediction. Conventional hazard maps tend to compile the neighbourhoods and dynamic types of the known past landslides, assuming that they satisfactorily represent the locations of future landslides. The latter,… (More)

Supervised image classification is based on assembling statistics between site-specific ground observations and remotely sensed measurements. If supervised image classification is applied within the context of a particular theme (e.g. vegetation, soil, lithology, land use), one is often confronted with extracting the statistical correlations from a… (More)

The most crucial but difficult task in the analysis of the risk due to landslide hazard is the estimation of the conditional probability of the occurrence of future landslides in a study area within a specific time period given the presence of spatial and geomorphologic features. This contribution explores a modeling procedure for estimating that… (More)

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