Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Landslide susceptibility hazard map in southwest Sweden using artificial neural network
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.ORCID iD: 0000-0001-5372-7519
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.ORCID iD: 0000-0002-8152-6092
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.ORCID iD: 0000-0001-9615-4861
2019 (English)In: Catena (Cremlingen. Print), ISSN 0341-8162, E-ISSN 1872-6887, Vol. 183, article id UNSP 104225Article in journal (Refereed) Published
Abstract [en]

Landslides as major geo-hazards in Sweden adversely impact on nearby environments and socio-economics. In this paper, a landslide susceptibility map using a proposed subdivision approach for a large area in southwest Sweden has been produced. The map has been generated by means of an artificial neural network (ANN) model developed using fourteen causative factors extracted from topographic and geomorphologic, geological, land use, hydrology and hydrogeology characteristics. The landslide inventory map includes 242 events identified from different validated resources and interpreted aerial photographs. The weights of the causative factors employed were analyzed and verified using accepted mathematical criteria, sensitivity analysis, previous studies, and actual landslides. The high accuracy achieved using the ANN model demonstrates a consistent criterion for future landslide susceptibility zonation. Comparisons with earlier susceptibility assessments in the area show the model to be a cost-effective and potentially vital tool for urban planners in developing cities and municipalities.

Place, publisher, year, edition, pages
ELSEVIER , 2019. Vol. 183, article id UNSP 104225
Keywords [en]
Landslide, GIS, Sweden, Artificial neural network
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262756DOI: 10.1016/j.catena.2019.104225ISI: 000488417700047Scopus ID: 2-s2.0-85071591343OAI: oai:DiVA.org:kth-262756DiVA, id: diva2:1365000
Note

QC 20191023

Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-10-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Abbaszadeh Shahri, AbbasSpross, JohanJohansson, FredrikLarsson, Stefan

Search in DiVA

By author/editor
Abbaszadeh Shahri, AbbasSpross, JohanJohansson, FredrikLarsson, Stefan
By organisation
Soil and Rock Mechanics
In the same journal
Catena (Cremlingen. Print)
Earth and Related Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 41 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf