Using the most similar case method to automatically select environmental covariates for predictive mapping

doi:10.1007/s12145-020-00466-5

	Using the most similar case method to automatically select environmental covariates for predictive mapping
	Liang, Peng 1,2; Qin, Cheng-Zhi 1,2,3; Zhu, A-Xing 1,2,3,4,5; Zhu, Tong-Xin 6; Fan, Nai-Qing 1,2; Hou, Zhi-Wei 1,2
刊名	EARTH SCIENCE INFORMATICS
	2020-05-02
页码	10
关键词	Predictive mapping Environmental covariates Case-based reasoning Leave-one-out cross-validation Digital soil mapping
ISSN号	1865-0473
DOI	10.1007/s12145-020-00466-5
通讯作者	Qin, Cheng-Zhi(qincz@lreis.ac.cn)
英文摘要	Predictive 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.
资助项目	National 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
WOS关键词	SOIL PROPERTIES ; KNOWLEDGE ; IMAGERY
WOS研究方向	Computer Science ; Geology
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000530169300001
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159944]
专题	中国科学院地理科学与资源研究所
通讯作者	Qin, Cheng-Zhi
作者单位	1.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
推荐引用方式 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.