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Machine Learning and Landslide Assessment in a GIS Environment

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GeoComputational Analysis and Modeling of Regional Systems

Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that include basic Machine Learning techniques (Artificial Neural Networks, Decision trees, Support Vector Machines) and additional useful procedures are described to show how they can be applied to address a complex problem such as landslide assessment. Two types of models are proposed in the work herein that are useful for describing landslide susceptibility and landslide prediction. The region of Halenkovice in Czech Republic is presented as a case study to illustrate and bring closer the practical aspects of landslide assessment. These aspects consider data preparation and preprocessing, scale effects, model optimization, and evaluation. The results show that Support Vector Machines and similar Machine Learning (ML) techniques can be successfully applied to address the zoning of landslide susceptibility, which might be an important breakthrough for potential applications in regional planning and decision-making.

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Notes

  1. 1.

    This open-source software can only be found on a supplementary CD of the book Kanevsky et al. 2009, which gives practical examples for using the software.

  2. 2.

    Input 2D raster datasets are organized in the way that each grid element (pixel) represents a data instance with attached spatial reference and supplementary thematic information (geological, morphometric, environmental and their derivatives).

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Acknowledgments

This work was supported by the Ministry of Science of the Republic of Serbia (Contracts No. III 47014 and TR 36009).

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Authors and Affiliations

  1. Faculty of Mining and Geology, University of Belgrade, Belgrade, Serbia

    Miloš Marjanović & Biljana Abolmasov

  2. Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia

    Branislav Bajat & Miloš Kovačević

Authors
  1. Miloš Marjanović
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  2. Branislav Bajat
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  3. Biljana Abolmasov
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  4. Miloš Kovačević
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Corresponding author

Correspondence to Branislav Bajat .

Editor information

Editors and Affiliations

  1. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina, USA

    Jean-Claude Thill

  2. Department of Geography, Simon Fraser University, Burnaby, British Columbia, Canada

    Suzana Dragicevic

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Marjanović, M., Bajat, B., Abolmasov, B., Kovačević, M. (2018). Machine Learning and Landslide Assessment in a GIS Environment. In: Thill, JC., Dragicevic, S. (eds) GeoComputational Analysis and Modeling of Regional Systems. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-59511-5_11

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