Ontology type: schema:ScholarlyArticle
2019-04
AUTHORSLei Chu, Liang-Jie Wang, Jiang Jiang, Xia Liu, Kazuhide Sawada, Jinchi Zhang
ABSTRACTLandslide susceptibility mapping (LSM) is a critical tool for mitigating the damages caused by geologic disasters. The selection of map units and mathematical models greatly affects the efficiency of LSM. To obtain the most appropriate combination of map units and mathematical models, four scales of catchment map units (CMUs) were analyzed and random forest (RF) and multivariate adaptive regression spline (MARSpline) models were applied in Gero City, Japan. The percentage of correctly identified landslides and the areas under the relative operating characteristic (ROC) curve were used to evaluate the model performances. The results indicate that the RF model had higher prediction accuracy than the MARSpline model, especially when the size of the CMU was 0.09 km2. A relatively high percentage of landslides fell into the high and very high landslide susceptibility classes (73%) and the lowest percentage of landslides fell into the very low landslide susceptibility classes (0.82%). The prediction-area (P-A) plots indicated that the prediction rates were higher for the RF model than the MARSpline model. The results of this study also suggest that the model accuracy can be increased if the appropriate CMU size is used. Therefore, the potential benefits of using the RF model in combination with the appropriate CMU size should be further explored using additional landslide-conditioning factors and other models. More... »
PAGES341-355
http://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8
DOIhttp://dx.doi.org/10.1007/s12303-018-0038-8
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1106542688
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0102",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Applied Mathematics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Nanjing Forestry University",
"id": "https://www.grid.ac/institutes/grid.410625.4",
"name": [
"Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, 210037, Nanjing, China",
"Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, 210037, Nanjing, China"
],
"type": "Organization"
},
"familyName": "Chu",
"givenName": "Lei",
"id": "sg:person.07356263601.01",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07356263601.01"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Nanjing Forestry University",
"id": "https://www.grid.ac/institutes/grid.410625.4",
"name": [
"Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, 210037, Nanjing, China",
"Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, 210037, Nanjing, China"
],
"type": "Organization"
},
"familyName": "Wang",
"givenName": "Liang-Jie",
"id": "sg:person.012017254627.44",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012017254627.44"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Nanjing Forestry University",
"id": "https://www.grid.ac/institutes/grid.410625.4",
"name": [
"Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, 210037, Nanjing, China",
"Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, 210037, Nanjing, China"
],
"type": "Organization"
},
"familyName": "Jiang",
"givenName": "Jiang",
"id": "sg:person.016052072721.00",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016052072721.00"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Nanjing Forestry University",
"id": "https://www.grid.ac/institutes/grid.410625.4",
"name": [
"Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, 210037, Nanjing, China",
"Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, 210037, Nanjing, China"
],
"type": "Organization"
},
"familyName": "Liu",
"givenName": "Xia",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Gifu University",
"id": "https://www.grid.ac/institutes/grid.256342.4",
"name": [
"Graduate School of Engineering, Gifu University, 5011193, Gifu, Japan"
],
"type": "Organization"
},
"familyName": "Sawada",
"givenName": "Kazuhide",
"id": "sg:person.016304500055.19",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016304500055.19"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Nanjing Forestry University",
"id": "https://www.grid.ac/institutes/grid.410625.4",
"name": [
"Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, 210037, Nanjing, China",
"Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, 210037, Nanjing, China"
],
"type": "Organization"
},
"familyName": "Zhang",
"givenName": "Jinchi",
"id": "sg:person.01231371562.02",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01231371562.02"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s12665-009-0394-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001904943",
"https://doi.org/10.1007/s12665-009-0394-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12665-009-0394-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001904943",
"https://doi.org/10.1007/s12665-009-0394-9"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/rs5115488",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002440683"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11069-014-1562-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003051939",
"https://doi.org/10.1007/s11069-014-1562-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.eswa.2010.12.167",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003712455"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jseaes.2012.12.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003757984"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.catena.2013.10.011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004249704"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2010.04.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005217860"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enggeo.2009.10.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007112515"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2010.10.012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008087502"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12303-014-0032-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008532919",
"https://doi.org/10.1007/s12303-014-0032-8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ecolind.2015.08.036",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010385631"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.catena.2013.08.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011587206"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12524-010-0020-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011705455",
"https://doi.org/10.1007/s12524-010-0020-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12524-010-0020-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011705455",
"https://doi.org/10.1007/s12524-010-0020-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12524-010-0020-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011705455",
"https://doi.org/10.1007/s12524-010-0020-z"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2012.04.023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012174794"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2005.06.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015480314"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2005.06.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015480314"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.gexplo.2015.10.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015858984"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2013.04.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016352699"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2012.01.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017140250"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.catena.2013.11.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018246700"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enggeo.2008.01.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018647843"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-012-0610-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019624469",
"https://doi.org/10.1007/s12517-012-0610-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2009.10.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022574682"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2015.07.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023575071"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1010933404324",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024739340",
"https://doi.org/10.1023/a:1010933404324"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00254-006-0491-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024793977",
"https://doi.org/10.1007/s00254-006-0491-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00254-006-0491-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024793977",
"https://doi.org/10.1007/s00254-006-0491-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s100440200011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025322802",
"https://doi.org/10.1007/s100440200011"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10346-013-0391-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025655969",
"https://doi.org/10.1007/s10346-013-0391-7"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jag.2013.09.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026544110"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2009.02.026",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027114246"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10346-008-0134-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029503294",
"https://doi.org/10.1007/s10346-008-0134-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10346-008-0134-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029503294",
"https://doi.org/10.1007/s10346-008-0134-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enggeo.2010.09.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030520851"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2009.09.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032205827"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-014-1554-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033388841",
"https://doi.org/10.1007/s12517-014-1554-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2010.09.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033621788"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2010.05.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035367280"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.catena.2015.10.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036406412"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2014.10.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037361050"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/srep09899",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038508053",
"https://doi.org/10.1038/srep09899"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jafrearsci.2016.04.019",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038610543"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2015.10.030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039191840"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-014-9261-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042370244",
"https://doi.org/10.1007/s11053-014-9261-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10346-012-0320-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042968034",
"https://doi.org/10.1007/s10346-012-0320-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2015.03.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045064995"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12303-015-0026-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046728420",
"https://doi.org/10.1007/s12303-015-0026-1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cageo.2012.08.023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047976418"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2006.09.023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048131515"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geomorph.2008.02.011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048533275"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12665-014-3661-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048740396",
"https://doi.org/10.1007/s12665-014-3661-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.catena.2011.01.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048987597"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tgrs.2007.912441",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061610424"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1198/tast.2009.08199",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064201606"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-04",
"datePublishedReg": "2019-04-01",
"description": "Landslide susceptibility mapping (LSM) is a critical tool for mitigating the damages caused by geologic disasters. The selection of map units and mathematical models greatly affects the efficiency of LSM. To obtain the most appropriate combination of map units and mathematical models, four scales of catchment map units (CMUs) were analyzed and random forest (RF) and multivariate adaptive regression spline (MARSpline) models were applied in Gero City, Japan. The percentage of correctly identified landslides and the areas under the relative operating characteristic (ROC) curve were used to evaluate the model performances. The results indicate that the RF model had higher prediction accuracy than the MARSpline model, especially when the size of the CMU was 0.09 km2. A relatively high percentage of landslides fell into the high and very high landslide susceptibility classes (73%) and the lowest percentage of landslides fell into the very low landslide susceptibility classes (0.82%). The prediction-area (P-A) plots indicated that the prediction rates were higher for the RF model than the MARSpline model. The results of this study also suggest that the model accuracy can be increased if the appropriate CMU size is used. Therefore, the potential benefits of using the RF model in combination with the appropriate CMU size should be further explored using additional landslide-conditioning factors and other models.",
"genre": "research_article",
"id": "sg:pub.10.1007/s12303-018-0038-8",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1136627",
"issn": [
"1226-4806",
"1598-7477"
],
"name": "Geosciences Journal",
"type": "Periodical"
},
{
"issueNumber": "2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "23"
}
],
"name": "Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units",
"pagination": "341-355",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"519e037935776ed4c3c1d7f62aa16e22f44e11213a89b840cdc01acc13a0b047"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s12303-018-0038-8"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1106542688"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s12303-018-0038-8",
"https://app.dimensions.ai/details/publication/pub.1106542688"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T09:34",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000346_0000000346/records_99815_00000004.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs12303-018-0038-8"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12303-018-0038-8'
This table displays all metadata directly associated to this object as RDF triples.
268 TRIPLES
21 PREDICATES
78 URIs
19 LITERALS
7 BLANK NODES