Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

Clicks: 198
ID: 261825
2019
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
Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF …
Reference Key
q2019thelandslide Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors He Q;Shahabi H;Shirzadi A;Li S;Chen W;Wang N;Chai H;Bian H;Ma J;Chen Y;Wang X;Chapi K;Ahmad BB;;
Journal The Science of the total environment
Year 2019
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/30708212
Keywords
National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine baharin bin ahmad pmid:30708212 doi:10.1016/j.scitotenv.2019.01.329 qingfeng he himan shahabi

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.