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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Ranked #182 of 862 articles by views in International journal of environmental research and public health

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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 …
Reference Key
vh2020internationalshallow Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
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
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Journal International journal of environmental research and public health
Year 2020
DOI
DOI not found
URL
https://www.pubmed.gov/32316191/
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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