Academic Journal
View in EDS
Linked Full Text

A prediction model for Xiangyang Neolithic sites based on a random forest algorithm

Bibliographic Details
Title: A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
Authors: Li Linzhi, Chen Xingyu, Sun Deliang, Wen Haijia
Source: Open Geosciences, Vol 15, Iss 1, Pp 1-5 (2023)
Publisher Information: De Gruyter, 2023.
Publication Year: 2023
Collection: LCC:Geology
Subject Terms: archaeological site prediction, random forest model, xiangyang city, hubei, Geology, QE1-996.5
More Details: The archaeological site prediction model can accurately identify archaeological site areas to enable better knowledge and understanding of human civilization processes and social development patterns. A total of 129 Neolithic site data in the region were collected using the Xiangyang area as the study area. An eight-factor index system of elevation, slope, slope direction, micromorphology, distance to water, slope position, planar curvature, and profile curvature was constructed. A geospatial database with a resolution of 30 m × 30 m was established. The whole sample set was built and trained in the ratio of 1:1 archaeological to nonarchaeological sites to obtain the prediction results. The average Gini coefficient was used to evaluate the influence of various archaeological site factors. The results revealed that the area under the curve values of the receiver operating characteristic curves were 1.000, 0.994, and 0.867 for the training, complete, and test datasets, respectively. Moreover, 60% of the historical, archaeological sites were located in the high-probability zone, accounting for 12% of the study area. The prediction model proposed in this study matched the spatial distribution characteristics of archaeological site locations. With the model assessed using the best samples, the results were categorized into three classes: low, average, and high. The proportion of low-, average-, and high-probability zones decreased in order. The high-probability zones were mainly located near the second and third tributaries and distributed at the low eastern hills and central hillocks. The random forest (RF) model was used to rank the importance of archaeological site variables. Elevation, slope, and micro-geomorphology were classified as the three most important variables. The RF model for archaeological site prediction has better stability and predictive ability in the case field; the model provides a new research method for archaeological site prediction and provides a reference for revealing the relationship between archaeological activities and the natural environment.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2391-5447
Relation: https://doaj.org/toc/2391-5447
DOI: 10.1515/geo-2022-0467
Access URL: https://doaj.org/article/dde95471c85a492a8b94d051d98c456b
Accession Number: edsdoj.95471c85a492a8b94d051d98c456b
Database: Directory of Open Access Journals
FullText Links:
  – Type: other
    Url: https://resolver.ebsco.com:443/public/rma-ftfapi/ejs/direct?AccessToken=4B6CAD5C0670D18B23FF&Show=Object
Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=23915447&ISBN=&volume=15&issue=1&date=20230301&spage=1&pages=1-5&title=Open Geosciences&atitle=A%20prediction%20model%20for%20Xiangyang%20Neolithic%20sites%20based%20on%20a%20random%20forest%20algorithm&aulast=Li%20Linzhi&id=DOI:10.1515/geo-2022-0467
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
  – Url: https://doaj.org/article/dde95471c85a492a8b94d051d98c456b
    Name: EDS - DOAJ (s8985755)
    Category: fullText
    Text: View record from DOAJ
    MouseOverText: View record from DOAJ
Header DbId: edsdoj
DbLabel: Directory of Open Access Journals
An: edsdoj.95471c85a492a8b94d051d98c456b
RelevancyScore: 986
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 986.003051757813
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li+Linzhi%22">Li Linzhi</searchLink><br /><searchLink fieldCode="AR" term="%22Chen+Xingyu%22">Chen Xingyu</searchLink><br /><searchLink fieldCode="AR" term="%22Sun+Deliang%22">Sun Deliang</searchLink><br /><searchLink fieldCode="AR" term="%22Wen+Haijia%22">Wen Haijia</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Open Geosciences, Vol 15, Iss 1, Pp 1-5 (2023)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: De Gruyter, 2023.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Geology
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22archaeological+site+prediction%22">archaeological site prediction</searchLink><br /><searchLink fieldCode="DE" term="%22random+forest+model%22">random forest model</searchLink><br /><searchLink fieldCode="DE" term="%22xiangyang+city%22">xiangyang city</searchLink><br /><searchLink fieldCode="DE" term="%22hubei%22">hubei</searchLink><br /><searchLink fieldCode="DE" term="%22Geology%22">Geology</searchLink><br /><searchLink fieldCode="DE" term="%22QE1-996%2E5%22">QE1-996.5</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: The archaeological site prediction model can accurately identify archaeological site areas to enable better knowledge and understanding of human civilization processes and social development patterns. A total of 129 Neolithic site data in the region were collected using the Xiangyang area as the study area. An eight-factor index system of elevation, slope, slope direction, micromorphology, distance to water, slope position, planar curvature, and profile curvature was constructed. A geospatial database with a resolution of 30 m × 30 m was established. The whole sample set was built and trained in the ratio of 1:1 archaeological to nonarchaeological sites to obtain the prediction results. The average Gini coefficient was used to evaluate the influence of various archaeological site factors. The results revealed that the area under the curve values of the receiver operating characteristic curves were 1.000, 0.994, and 0.867 for the training, complete, and test datasets, respectively. Moreover, 60% of the historical, archaeological sites were located in the high-probability zone, accounting for 12% of the study area. The prediction model proposed in this study matched the spatial distribution characteristics of archaeological site locations. With the model assessed using the best samples, the results were categorized into three classes: low, average, and high. The proportion of low-, average-, and high-probability zones decreased in order. The high-probability zones were mainly located near the second and third tributaries and distributed at the low eastern hills and central hillocks. The random forest (RF) model was used to rank the importance of archaeological site variables. Elevation, slope, and micro-geomorphology were classified as the three most important variables. The RF model for archaeological site prediction has better stability and predictive ability in the case field; the model provides a new research method for archaeological site prediction and provides a reference for revealing the relationship between archaeological activities and the natural environment.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic resource
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2391-5447
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://doaj.org/toc/2391-5447
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1515/geo-2022-0467
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/dde95471c85a492a8b94d051d98c456b" linkWindow="_blank">https://doaj.org/article/dde95471c85a492a8b94d051d98c456b</link>
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsdoj.95471c85a492a8b94d051d98c456b
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.95471c85a492a8b94d051d98c456b
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1515/geo-2022-0467
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 1
    Subjects:
      – SubjectFull: archaeological site prediction
        Type: general
      – SubjectFull: random forest model
        Type: general
      – SubjectFull: xiangyang city
        Type: general
      – SubjectFull: hubei
        Type: general
      – SubjectFull: Geology
        Type: general
      – SubjectFull: QE1-996.5
        Type: general
    Titles:
      – TitleFull: A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li Linzhi
      – PersonEntity:
          Name:
            NameFull: Chen Xingyu
      – PersonEntity:
          Name:
            NameFull: Sun Deliang
      – PersonEntity:
          Name:
            NameFull: Wen Haijia
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 23915447
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Open Geosciences
              Type: main
ResultId 1