IGSNRR OpenIR
Using the most similar case method to automatically select environmental covariates for predictive mapping
Liang, Peng1,2; Qin, Cheng-Zhi1,2,3; Zhu, A-Xing1,2,3,4,5; Zhu, Tong-Xin6; Fan, Nai-Qing1,2; Hou, Zhi-Wei1,2
2020-05-02
Source PublicationEARTH SCIENCE INFORMATICS
ISSN1865-0473
Pages10
Corresponding AuthorQin, Cheng-Zhi(qincz@lreis.ac.cn)
AbstractPredictive mapping of environment is an important means for environment assessment and management. The selection of predictor variables (or environmental covariates) is the first and key step in predictive mapping. A number of machine learning and statistical models have been developed to select what and how many environmental covariates in a wide range of predictive mapping. Nevertheless, those models require a large amount of field data for model training and calibration, which can be problematic in applying to the areas with no or very limited field data available. To overcome the shortcoming, this paper proposes the most similar case method for selecting environmental covariates for predictive mapping. First, we describe the basic idea and the development procedures of the most similar case method; second, as an experimental test, we employ the proposed method to select the topographic covariates for inputting to the predictive soil mapping; third, we evaluate the effectiveness of the proposed method in the designed experiment using the leave-one-out cross-validation method. In total, 191 evaluation cases are included in the experimental case base and the test results show that 58.7% of the topographic covariates originally used in each evaluation case are correctly selected by the proposed method, which suggests that the proposed most-similar-case method perform reasonably well even with a relatively limited size of the case base. The future work should include the selection of other types of environmental covariates (e.g., climate, organism, etc.) and the development of an automatic method to extract the existing application cases from literature.
KeywordPredictive mapping Environmental covariates Case-based reasoning Leave-one-out cross-validation Digital soil mapping
DOI10.1007/s12145-020-00466-5
WOS KeywordSOIL PROPERTIES ; KNOWLEDGE ; IMAGERY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41871300] ; National Key R&D Program of China[2016YFC0500205] ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award from the University of Wisconsin-Madison ; Hammel Faculty Fellow Award from the University of Wisconsin-Madison ; Manasse Chair Professorship from the University of Wisconsin-Madison
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award from the University of Wisconsin-Madison ; Hammel Faculty Fellow Award from the University of Wisconsin-Madison ; Manasse Chair Professorship from the University of Wisconsin-Madison
WOS Research AreaComputer Science ; Geology
WOS SubjectComputer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS IDWOS:000530169300001
PublisherSPRINGER HEIDELBERG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/159945
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQin, Cheng-Zhi
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
4.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
6.Univ Minnesota, Dept Geog & Philosophy, Duluth, MN 55812 USA
Recommended Citation
GB/T 7714
Liang, Peng,Qin, Cheng-Zhi,Zhu, A-Xing,et al. Using the most similar case method to automatically select environmental covariates for predictive mapping[J]. EARTH SCIENCE INFORMATICS,2020:10.
APA Liang, Peng,Qin, Cheng-Zhi,Zhu, A-Xing,Zhu, Tong-Xin,Fan, Nai-Qing,&Hou, Zhi-Wei.(2020).Using the most similar case method to automatically select environmental covariates for predictive mapping.EARTH SCIENCE INFORMATICS,10.
MLA Liang, Peng,et al."Using the most similar case method to automatically select environmental covariates for predictive mapping".EARTH SCIENCE INFORMATICS (2020):10.
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