{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361699995917618048.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.5194/nhess-5-853-2005"}},{"identifier":{"@type":"URI","@value":"https://nhess.copernicus.org/articles/5/853/2005/nhess-5-853-2005.pdf"}}],"dc:title":[{"@value":"Spatial prediction models for landslide hazards: review, comparison and evaluation"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>Abstract. The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting \"present\" and \"future\" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.\n                    </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380849946840558213","@type":"Researcher","foaf:name":[{"@value":"A. Brenning"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"16849981"}],"prism:publicationName":[{"@value":"Natural Hazards and Earth System Sciences"}],"dc:publisher":[{"@value":"Copernicus GmbH"}],"prism:publicationDate":"2005-11-07","prism:volume":"5","prism:number":"6","prism:startingPage":"853","prism:endingPage":"862"},"reviewed":"false","dc:rights":["https://creativecommons.org/licenses/by-nc-sa/2.5/"],"url":[{"@id":"https://nhess.copernicus.org/articles/5/853/2005/nhess-5-853-2005.pdf"}],"createdAt":"2010-04-29","modifiedAt":"2021-01-29","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360285710157427712","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Big Data Analytics for Emergency Communication Networks: A Survey"}]},{"@id":"https://cir.nii.ac.jp/crid/1360846645913724672","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest"}]},{"@id":"https://cir.nii.ac.jp/crid/1360869856022152192","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Quantitative analysis of landslide impact on vegetation: Insights from field surveys and UAV imagery"}]},{"@id":"https://cir.nii.ac.jp/crid/2051151842044695040","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Scaling land-surface variables for landslide detection"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.5194/nhess-5-853-2005"},{"@type":"CROSSREF","@value":"10.4236/ijg.2016.75056_references_DOI_TTeXJ6eRZmbx43mqrIUbpOfLl6w"},{"@type":"CROSSREF","@value":"10.1109/comst.2016.2540004_references_DOI_TTeXJ6eRZmbx43mqrIUbpOfLl6w"},{"@type":"CROSSREF","@value":"10.1186/s40645-019-0290-1_references_DOI_TTeXJ6eRZmbx43mqrIUbpOfLl6w"},{"@type":"CROSSREF","@value":"10.1016/j.ecoleng.2025.107581_references_DOI_TTeXJ6eRZmbx43mqrIUbpOfLl6w"}]}