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Landslide Susceptibility Prediction Maps: From Blind-Testing to Uncertainty of Class Membership: A Review of Past and Present Developments

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Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques

Part of the book series: Advances in Natural and Technological Hazards Research ((NTHR,volume 48))

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Abstract

This contribution reviews the spatial characterization originally stimulated by mineral exploration and later by environmental concern. Research network programmes of the European Commission triggered cross-breeding of disciplines and approaches to hazard prediction in particular for the Deba Valley study area in northern Spain. Examples of results of spatial prediction modelling using blind tests to obtain prediction-rate curves and uncertainty patterns allow considerations on the role of such modelling for research, surveying and civil protection.

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Acknowledgements

We are grateful to an anonymous reviewer and RAC Garcia who helped in correcting and improving this manuscript.

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Authors and Affiliations

  1. DISAT, University of Milano-Bicocca, Milan, Italy

    Andrea G. Fabbri

  2. SpatialModels Inc, Ottawa, Canada

    Chang-Jo Chung

Authors
  1. Andrea G. Fabbri
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  2. Chang-Jo Chung
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Correspondence to Andrea G. Fabbri .

Editor information

Editors and Affiliations

  1. Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. IRPI, National Research Council, Perugia, Italy

    Mauro Rossi

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Fabbri, A.G., Chung, CJ. (2019). Landslide Susceptibility Prediction Maps: From Blind-Testing to Uncertainty of Class Membership: A Review of Past and Present Developments. In: Pourghasemi, H., Rossi, M. (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-73383-8_6

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