Spatial target mapping: an approach to susceptibility prediction based on iterative crossvalidations

Abstract

This contribution proposes iterative cross-validation as an approach to assess the quality of spatial predictions of hazardous events. Given the complexity of mathematical procedures and the diversity of geomorphologic applications made to date, STM, the Spatial Target Mapping, is a piece of software, ancillary to a geographical information system and a spreadsheet, that constrains such complexity into a clearly structured framework optimized for modelling. Spatial relationships are established between the distribution of hazardous occurrences and their physical settings to represent in part the slope failure process. They are used in the modelling to anticipate the location of future occurrences. Procedural aspects and computational options are discussed by means of an application to a database developed for landslide susceptibility prediction in northern Italy. Two mathematical models of spatial relationships, fuzzy set function and logistic discriminant function, are applied to generate prediction patterns, prediction-rate tables, and subsequently compute target and uncertainty patterns. The two processing strategies used are sequential elimination and random selection of occurrences for iterative crossvalidations.