Machine Learning and Landslide Assessment in a GIS Environment

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


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.


Landslide susceptibility SVM ANN Decision Trees Cross-scaling 



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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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Mining and GeologyUniversity of BelgradeBelgradeSerbia
  2. 2.Faculty of Civil EngineeringUniversity of BelgradeBelgradeSerbia

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