Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Clicks: 244
ID: 113021
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
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;;
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

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.