Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Alev MUTLU; Furkan Goz; Kubra Koksal; Arzu ERENER

doi:https://doi.org/10.16984/saufenbilder.479801

Makale detayları

Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Yazarlar:

Alev MUTLU | Furkan Goz | Kubra Koksal | Arzu ERENER

Ana Başlıklar:

Veri işleme & bilgisayar bilimi | Ekoloji

Dergi:

Sakarya University Journal of Science 2019 Vol.23 Issue 5, pp.782-787

Belge Tipi:

Makale

Yayın Tarihi:

9/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000557069

DOI:

https://doi.org/10.16984/saufenbilder.479801

Özet:

 


Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set show that the proposed method achieves 83.07% classification accuracy and is superior over logistic regression, support vector machine and neural network based approaches and achieves similar results when compared to a decision trees-based model. 


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Makale detayları

Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Yazarlar: Alev MUTLU | Furkan Goz | Kubra Koksal | Arzu ERENER

Ana Başlıklar: Veri işleme & bilgisayar bilimi | Ekoloji

Dergi: Sakarya University Journal of Science 2019 Vol.23 Issue 5, pp.782-787

Belge Tipi: Makale

Yayın Tarihi: 9/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000557069

DOI: https://doi.org/10.16984/saufenbilder.479801

Sayı

Yazarlar:

Alev MUTLU | Furkan Goz | Kubra Koksal | Arzu ERENER

Ana Başlıklar:

Veri işleme & bilgisayar bilimi | Ekoloji

Dergi:

Sakarya University Journal of Science 2019 Vol.23 Issue 5, pp.782-787

Belge Tipi:

Makale

Yayın Tarihi:

9/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000557069

DOI:

https://doi.org/10.16984/saufenbilder.479801