[Assessing soil Zn content using decision tree analysis].
Identifieur interne : 000312 ( PubMed/Corpus ); précédent : 000311; suivant : 000313[Assessing soil Zn content using decision tree analysis].
Auteurs : Xiu-Ying Zhang ; Qi Sun ; Ke Wang ; Yu-Gen Jiang ; Fen-Fang Lin ; Ning HanSource :
- Huan jing ke xue= Huanjing kexue [ 0250-3301 ] ; 2008.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : Soil, Soil Pollutants, Zinc.
- geographic : China.
- Decision Trees, Environmental Monitoring.
Abstract
Taking Fuyang county of Zhejiang Province as the study area, the present study estimated soil Zn concentration (divided by its local background value into G1, G2, G3, G4 and G5) using CART methods, based on 184 soil samples (0-20 cm). The environmental factors used to infer the Zn concentration rules included soil types, pH, organic matter, agricultural land uses, industry plant types, road and village density. The other 41 soil samples were used to test the prediction results. The Zn concentration classes estimated by CART have accuracy in attribution to the right classes of 80.49%. This is a 21.95% improvement on Zn classes estimated by ordinary Kriging method. Concretely, it improved the precision much for G1, G3 and G4, while obtained similar precision for G2 and G5. Moreover, CART provided some insights into the sources of current soil Zn contents. The categories of industrial plants play the most important role in separating the high and low level of Zn concentration (G1, G2 and G3, G4, G5), and the pH value, soil types and agricultural types play important roles in differentiation among G1 and G2, and among G3, G4 and G5.
PubMed: 19256393
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pubmed:19256393Le document en format XML
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The environmental factors used to infer the Zn concentration rules included soil types, pH, organic matter, agricultural land uses, industry plant types, road and village density. The other 41 soil samples were used to test the prediction results. The Zn concentration classes estimated by CART have accuracy in attribution to the right classes of 80.49%. This is a 21.95% improvement on Zn classes estimated by ordinary Kriging method. Concretely, it improved the precision much for G1, G3 and G4, while obtained similar precision for G2 and G5. Moreover, CART provided some insights into the sources of current soil Zn contents. The categories of industrial plants play the most important role in separating the high and low level of Zn concentration (G1, G2 and G3, G4, G5), and the pH value, soil types and agricultural types play important roles in differentiation among G1 and G2, and among G3, G4 and G5.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19256393</PMID><DateCreated><Year>2009</Year><Month>03</Month><Day>04</Day></DateCreated><DateCompleted><Year>2009</Year><Month>10</Month><Day>27</Day></DateCompleted><DateRevised><Year>2016</Year><Month>10</Month><Day>18</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0250-3301</ISSN><JournalIssue CitedMedium="Print"><Volume>29</Volume><Issue>12</Issue><PubDate><Year>2008</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Huan jing ke xue= Huanjing kexue</Title><ISOAbbreviation>Huan Jing Ke Xue</ISOAbbreviation></Journal><ArticleTitle>[Assessing soil Zn content using decision tree analysis].</ArticleTitle><Pagination><MedlinePgn>3508-12</MedlinePgn></Pagination><Abstract><AbstractText>Taking Fuyang county of Zhejiang Province as the study area, the present study estimated soil Zn concentration (divided by its local background value into G1, G2, G3, G4 and G5) using CART methods, based on 184 soil samples (0-20 cm). The environmental factors used to infer the Zn concentration rules included soil types, pH, organic matter, agricultural land uses, industry plant types, road and village density. The other 41 soil samples were used to test the prediction results. The Zn concentration classes estimated by CART have accuracy in attribution to the right classes of 80.49%. This is a 21.95% improvement on Zn classes estimated by ordinary Kriging method. Concretely, it improved the precision much for G1, G3 and G4, while obtained similar precision for G2 and G5. Moreover, CART provided some insights into the sources of current soil Zn contents. The categories of industrial plants play the most important role in separating the high and low level of Zn concentration (G1, G2 and G3, G4, G5), and the pH value, soil types and agricultural types play important roles in differentiation among G1 and G2, and among G3, G4 and G5.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Xiu-ying</ForeName><Initials>XY</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Qi</ForeName><Initials>Q</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Ke</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Yu-gen</ForeName><Initials>YG</Initials></Author><Author ValidYN="Y"><LastName>Lin</LastName><ForeName>Fen-fang</ForeName><Initials>FF</Initials></Author><Author ValidYN="Y"><LastName>Han</LastName><ForeName>Ning</ForeName><Initials>N</Initials></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Huan Jing Ke Xue</MedlineTA><NlmUniqueID>8405344</NlmUniqueID><ISSNLinking>0250-3301</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012987">Soil</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012989">Soil Pollutants</NameOfSubstance></Chemical><Chemical><RegistryNumber>J41CSQ7QDS</RegistryNumber><NameOfSubstance UI="D015032">Zinc</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003663" MajorTopicYN="Y">Decision Trees</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012987" MajorTopicYN="N">Soil</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012989" MajorTopicYN="N">Soil Pollutants</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015032" MajorTopicYN="N">Zinc</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>3</Month><Day>5</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2009</Year><Month>3</Month><Day>5</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>10</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19256393</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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