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Automatic approach to deriving fuzzy slope positions
Zhu, Liang-Jun1,2; Zhu, A-Xing1,3,4,5,6; Qin, Cheng-Zhi1,2,6; Liu, Jun-Zhi3,4,6
2018-03-01
Source PublicationGEOMORPHOLOGY
ISSN0169-555X
Volume304Pages:173-183
Corresponding AuthorQin, Cheng-Zhi(qincz@lreis.ac.cn)
AbstractFuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions. (C) 2018 Elsevier B.V. All rights reserved.
KeywordSlope position Fuzzy membership Automation Parallel computing
DOI10.1016/j.geomorph.2017.12.024
WOS KeywordLANDFORM ELEMENTS ; TERRAIN ANALYSIS ; CLASSIFICATION ; SEGMENTATION ; GEOMETRY ; LOGIC ; DEMS
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[41422109] ; Natural Science Foundation of China[41431177] ; Innovation Project of LREIS[O88RA20CYA] ; Natural Science Foundation of Jiangsu Province of China[BK20150975]
Funding OrganizationNatural Science Foundation of China ; Innovation Project of LREIS ; Natural Science Foundation of Jiangsu Province of China
WOS Research AreaPhysical Geography ; Geology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary
WOS IDWOS:000426023400014
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56995
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, Beijing 100049, Peoples R China
3.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China
4.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China
5.Univ Wisconsin Madison, Dept Geog, Madison, WI 53706 USA
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Liang-Jun,Zhu, A-Xing,Qin, Cheng-Zhi,et al. Automatic approach to deriving fuzzy slope positions[J]. GEOMORPHOLOGY,2018,304:173-183.
APA Zhu, Liang-Jun,Zhu, A-Xing,Qin, Cheng-Zhi,&Liu, Jun-Zhi.(2018).Automatic approach to deriving fuzzy slope positions.GEOMORPHOLOGY,304,173-183.
MLA Zhu, Liang-Jun,et al."Automatic approach to deriving fuzzy slope positions".GEOMORPHOLOGY 304(2018):173-183.
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