Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
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ID: 113021
2020
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Abstract
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use manage …
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| Reference Key |
vh2020internationalshallow
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|---|---|
| Authors | Nhu VH;Shirzadi A;Shahabi H;Singh SK;Al-Ansari N;Clague JJ;Jaafari A;Chen W;Miraki S;Dou J;Luu C;Górski K;Thai Pham B;Nguyen HD;Ahmad BB;; |
| Journal | International journal of environmental research and public health |
| Year | 2020 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
National Center for Biotechnology Information
NCBI
NLM
MEDLINE
pubmed abstract
nih
national institutes of health
national library of medicine
pmid:32316191
pmc7215797
doi:10.3390/ijerph17082749
viet-ha nhu
ataollah shirzadi
baharin bin ahmad
artificial intelligence
Iran
goodness-of-fit
prediction accuracy
logistic model tree
shallow landslide
|
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