Probabilistic prediction models for landslide hazard mapping

Abstract

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 multi-layer spatial data in the study area was used to construct the prediction maps. The predictions were then cross-validated by comparing them with the remaining later landslides. When the database falls short of providing sufficient support for the prediction, the model allows the introduction of the expert's knowledge to modify the observed frequencies of the landslides with respect to the spatial data. The additional information should improve the prediction results. A case study from the Rio Chincina region in Colombia was used to illustrate the methodologies.