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dc.contributor.authorYang, Z.
dc.contributor.authorLiu, C.
dc.contributor.authorNie, R.
dc.contributor.authorZhang, W.
dc.contributor.authorZhang, L.
dc.contributor.authorZhang, Z.
dc.contributor.authorLi, W.
dc.contributor.authorLiu, G.
dc.contributor.authorDai, X.
dc.contributor.authorZhang, D.
dc.contributor.authorZhang, M.
dc.contributor.authorMiao, S.
dc.contributor.authorFu, X.
dc.contributor.authorRen, Z.
dc.contributor.authorLu, H.
dc.date.accessioned2022-10-24T23:51:17Z
dc.date.available2022-10-24T23:51:17Z
dc.date.issued2022
dc.identifier.citationYang, Z., Liu, C., Nie, R., Zhang, W., Zhang, L., Zhang, Z., Li, W., Liu, G., Dai, X., Zhang, D., Zhang, M., Miao, S., Fu, X., Ren, Z., & Lu, H. (2022). Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis. Remote Sensing, 14(16).
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs14163879
dc.identifier.urihttp://hdl.handle.net/10150/666477
dc.description.abstractLandslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. © 2022 by the authors.
dc.language.isoen
dc.publisherMDPI
dc.rightsCopyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectbibliometric analysis
dc.subjectCtiespace
dc.subjectknowledge graph
dc.subjectlandslide
dc.subjectsusceptibility prediction
dc.subjectuncertainty analysis
dc.subjectVOSviewer
dc.titleResearch on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis
dc.typeArticle
dc.typetext
dc.contributor.departmentSchool of Geography, Development & Environment, University of Arizona
dc.identifier.journalRemote Sensing
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleRemote Sensing
refterms.dateFOA2022-10-24T23:51:17Z


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Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).