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  • 1
    Electronic Resource
    Electronic Resource
    The representation of geoscience information for data integration (1993)
    Springer
    Natural resources research 2 (1993), S. 122-139 
    Details
    ISSN: 1573-8981
    Keywords: Dempster-Shafer belief function ; Representation ; Geographic information system (GIS) ; Data integration ; Spatially distributed map pattern ; Favorability function ; Fuzzy set ; Certainty factor ; Geopotential map ; Prediction ; Natural resources ; Natural hazard ; Probability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences
    Notes: Abstract In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Today's PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact). Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them. Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02272809
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Shape analysis and multispectral classification in geological remote sensing (1993)
    Springer
    Mathematical geology 25 (1993), S. 773-793 
    Details
    ISSN: 1573-8868
    Keywords: mathematical morphology ; synthetic measurements of shape ; image processing ; GIS
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract This paper discusses the usage of mathematical morphology in image processing of remotely-sensed data for geologic interpretation. Particular attention is given to noise-reducing transformations of spectral bands before and after different methods of classification, and to the usage of textural context. The development of a viable processing strategy requires a multidisciplinary approach and expert knowledge in different areas: (a) geology, geomorphology, and vegetation in a study area, (b) properties of the sensor for imagery photointerpretation, (c) spectral/spatial properties of the digital data within an integrated dataset (remote sensing and ancillary data), and (d) data-processing tools including mathematical morphology theory. Examples of geometric characterization of Canadian LANDSAT scenes are described in which shape measurements are obtained using a PC-based hybrid image-processing and geographic information system, termed ILWIS, which was developed at ITC, in the Netherlands. Classes from supervised and unsupervised classification are compared to guide in geological mapping. Classes over individual occurrences of broad vegetation-landform units are studied to aid in environmental mapping. Field knowledge is the context necessary to construct expert procedures to drive sequences of data-processing steps toward a target result such as optimal classification, enhancement, or feature extraction. The interaction between expert rules and the image-processing steps can be based on synthetic measurements of shape to quantize the information either spatially or spectrally. Many useful geometrical transformations of spatially-distributed data are extensions or generalizations of spatial analysis functions typical of geographic information systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00891043
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  • 3
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    Modelling aquifer vulnerability to nitrates under the assumption of varying spatial support of water well distribution (2021)
    WIT Press
    Details
    Publication Date: 2021-01-19
    Description: This contribution analyses the spatial support of sampling points used to express the presence or absence of NO3 ˉ pollution in the water table. A spatial database constructed for the assessment of ground water vulnerability is re-analysed with a different predictive strategy. In practice, a case study area surrounding the city of Milan in northern Italy becomes an opportunity to point at a very general prediction modelling problem in which the basic direct evidence of a process is obtained only by sampling with point like measurements of nitrate concentration, as the ones from drill holes or water wells. The main questions are: “What is the functional spatial support for the modelling?” and “What happens if different spatial supports are assumed?” The answers to these questions are counterintuitive. Over the area of study of about 2,000 km2 , the distribution of 305 water wells delimits a training area in which 133 wells are considered as impacted by nitrate pollution, i.e., direct supporting patterns of the modelling. The remaining 172 wells are considered as non-impacted. In the training area, nine natural and anthropogenic map data are assumed, as indirect supporting patterns of the modelling, to reflect both the potential source of nitrates and the relative ease in which nitrates may migrate in ground water. They cover the entire area of study. A mathematical model is used that computes spatial relationships between the direct and indirect supporting patterns based on empirical likelihood ratios. The relationships are integrated into prediction patterns and, by iterative cross-validations, into target and uncertainty patterns. These are then extended from the training area over the remaining much larger study areas for analysis and visualization. Square neighbourhoods of dimensions 20 × 20 m, 60 × 60 m, 180 × 180 m and 1,020 × 1,020 m around the 305 wells are used to delimit four training areas of different sizes. Surprisingly, the smaller spatial support appears as the most reliable.
    Description: Published
    Description: Online
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Keywords: Risk Analysis ; Hazard Mitigation ; Modelling ; Acquifer vulnerability ; nitrate pollution ; empirical likelihood ratios ; spatial support ; prediction-rate curves ; prediction patterns ; uncertainty patterns ; Risk Analysis and Hazard Mitigation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
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  • 4
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    Estimation of information loss when maskingconditional dependence and categorizingcontinuous data: further experiments on adatabase for spatial prediction modelling innorthern Italy (2020)
    Springer | Springer
    Details
    Publication Date: 2020-02-11
    Description: 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.
    Description: Published
    Description: 291-294
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Description: N/A or not JCR
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 5
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    Favourability modelling of landslide hazard with spatial uncertainty ofclass membership: a reapplication in central Slovenia (2020)
    Details
    Publication Date: 2020-02-10
    Description: 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.
    Description: Published
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Description: N/A or not JCR
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 6
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    Comparing Patterns of Spatial Relationships for Susceptibility Prediction of Landslide Occurrences (2018)
    Springer
    Details
    Publication Date: 2018-03-08
    Description: 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.
    Description: Published
    Description: 1135-1144
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Keywords: Landslide susceptibility, spatial support, spatial relationships, prediction models, prediction patterns, target pattern, ranked classes, cross-validation, database signature ; 04.04. Geology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 7
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    Spatial target mapping: an approach to susceptibility prediction based on iterative crossvalidations (2018)
    Details
    Publication Date: 2018-03-12
    Description: 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.
    Description: Published
    Description: Santander, Spain
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Keywords: Prediction mapping ; Landslide ; Database ; Geographical Information System ; Prediction mapping
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
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  • 8
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    Comparing Patterns of Spatial Relationships for Susceptibility Prediction of Landslide Occurrences (2018)
    Springer International
    Details
    Publication Date: 2018-03-12
    Description: 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.
    Description: Published
    Description: Ljubljana, Slovenia
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Keywords: Prediction mapping ; Landslide ; Database ; Geographical Information System ; Advanced in Landslide Science
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
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  • 9
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    Spatial Uncertainty of Target Patterns Generated by Different Prediction Models of Landslide Susceptibility (2022)
    MDPI
    Details
    Publication Date: 2022-02-25
    Description: his contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database constructed for the modeling, direct evidence is the presence of landslide trigger areas, while indirect evidence is the presence of corresponding multivariate context in the form of digital maps. Five mathematical modeling functions are applied to capture and integrate evidence, indirect and direct, for separating landslide-presence areas from the areas of landslide assumed absence. Empirical likelihood ratios are used first to represent the spatial relationships. These are then combined by the models into prediction scores, ordered, equal-area ranked, displayed, and synthesized as prediction-rate curves. A critical task is assessing how uncertainty levels vary across the different prediction patterns, i.e., the modeling results visualized as fixed, colored groups of ranks. This is obtained by a strategy of iterative cross validation that uses only part of the direct evidence to model the pattern and the rest to validate it as a predictor. The conducted experiments in a mountainous area in northern Italy point at a research challenge that can now be confronted with relative rank-based statistics and iterative cross-validation processes. The uncertainty properties of prediction patterns are mostly unknown nevertheless they are critical for interpreting and justifying prediction results.
    Description: This contribution was initially and partly supported by the European Commission Project “Mountain Risks: from Prediction to Management and Governance” (MRTN-CT-2006-035978, 2007–2010), Mountainrisk (2007).
    Description: Published
    Description: 3341
    Description: 2TR. Ricostruzione e modellazione della struttura crostale
    Description: JCR Journal
    Keywords: ranking ; uncertainty pattern ; landslide susceptibility ; cross validation ; prediction pattern ; target pattern ; prediction model ; Landslides
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 10
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    On Blind Tests and Spatial Prediction Models (2008)
    Springer
    Details
    Publication Date: 2008-05-28
    Print ISSN: 1520-7439
    Electronic ISSN: 1573-8981
    Topics: Geosciences
    Published by Springer
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