GraFar - Repository of the Faculty of Civil Engineering
Faculty of Civil Engineering of the University of Belgrade
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrilic)
    • Serbian (Latin)
  • Login
View Item 
  •   GraFar
  • GraFar
  • Radovi istraživača / Researcher's publications
  • View Item
  •   GraFar
  • GraFar
  • Radovi istraživača / Researcher's publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Machine Learning and Landslide Assessment in a GIS Environment

No Thumbnail
Authors
Đurić, Uroš
Bajat, Branislav
Abolmasov, Biljana
Kovačević, Miloš
Book part (Published version)
Metadata
Show full item record
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 resul...ts 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.

Source:
GeoComputational Analysis and Modeling of Regional Systems, 2018, 191-213
Publisher:
  • Cham: Springer International Publishing
Note:
  • Thill, Jean-Claude; Dragicevic, Suzana

DOI: 10.1007/978-3-319-59511-5_11

ISBN: 978-3-319-59511-5

[ Google Scholar ]
URI
http://grafar.grf.bg.ac.rs/handle/123456789/1101
Collections
  • Radovi istraživača / Researcher's publications
  • Катедра за геодезију и геоинформатику
  • Катедра за грађевинску геотехнику
Institution
GraFar

DSpace software copyright © 2002-2015  DuraSpace
About GraFar - Repository of the Faculty of Civil Engineering | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceInstitutionsAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About GraFar - Repository of the Faculty of Civil Engineering | Send Feedback

OpenAIRERCUB