Chung, Chang-Jo

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.

This contribution proposes a cautious way of constructing the susceptibility classes obtained from favourability modeling of landslide occurrences. It is based on the ranks of the numerical values obtained by the modelling. Such ranks can be displayed in the form of histograms, cumulative curves, and prediction patterns resembling maps. A number of models have been proposed and in this contribution the following will be compared in terms of their respective rankings for equal area classes: fuzzy set function, empirical likelihood ratio, linear and logistic regression, and Bayesian prediction function. The analyses performed and contrasted exemplify a generalized methodology for comparing predictions that should allow evaluating prediction patterns from any model. Unfortunately, many applications in the scientific literature use methods of characterizing prediction quality that make comparison hard or impossible. A database from a study area in the Mountain Community of Tirano in Valtellina, Lombardy Region, northern Italy, is used to illustrate how the results of the different models and strategies of analysis show the relevance of the properties of the database over those of the models.

Prediction patterns are generated using different data sets from a database for landslides hazard in northern Italy. A direct supporting pattern of the distribution of 28 complex landslides was previously used to obtain their spatial relationships with five categorical indirect supporting patterns representing the spatial context of the landslides: geology, land use, and permeability in addition to internal relief and slope, the latter two categorized into five classes. The five indirect supporting patterns were selected to minimize the effects of conditional dependence on prediction patterns by a Weight-of-Evidence model. The same set of patterns is reanalysed applying the Empirical Likelihood Ratio model using also uncategorized continuous supporting patterns: aspect, curvature, and digital elevation, in addition to internal relief and slope. The resulting prediction patterns are compared in terms of prediction rates and target-uncertainty patterns.

This contribution stems from the exposure of two different approaches to the representation of natural hazard: regression analysis on one side and favourability models on the other. As a consequence a spatial database for landslide hazard prediction in central Slovenia was shared to experiment on spatial prediction via cross-validation techniques. Due to the peculiarities of the database three types of analyses were selected: (i) predictions using an Empirical Likelihood Ratio model and four types of landslides in a training area, and extended to a surrounding study area; (ii) iterative cross-validations to obtain target, uncertainty and their combination patterns; and (iii) separation of one type of landslides into two groups of well predicted and poorly predicted occurrences by a cross-validation with the target pattern. The importance is underlined of sharing databases to encourage broader views of methodologies and strategies in spatial modelling.

This contribution proposes a cautious way of constructing the susceptibility classes obtained from favourability modeling of landslide occurrences. It is based on the ranks of the numerical values obtained by the modelling. Such ranks can be displayed in the form of histograms, cumulative curves, and prediction patterns resembling maps. A number of models have been proposed and in this contribution the following will be compared in terms of their respective rankings for equal area classes: fuzzy set function, empirical likelihood ratio, linear and logistic regression, and Bayesian prediction function. The analyses performed and contrasted exemplify a generalized methodology for comparing predictions that should allow evaluating prediction patterns from any model. Unfortunately, many applications in the scientific literature use methods of characterizing prediction quality that make comparison hard or impossible. A database from a study area in the Mountain Community of Tirano in Valtellina, Lombardy Region, northern Italy, is used to illustrate how the results of the different models and strategies of analysis show the relevance of the properties of the database over those of the models.