Comparison on two case‐based reasoning strategies of automatically selecting terrain covariates for digital soil mapping. Issue 5 (22nd August 2021)

• Record Type: Journal Article
• Authors: Liang, Peng; Qin, Cheng‐Zhi; Zhu, A‐Xing
• Is Part Of: Transactions in GIS. Volume 25:Issue 5(2021)
• Page Start: 2419
• Page End: 2437
• Publication Date: 2021-08-22
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.12831
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: The terrain covariate is one type of key environmental covariate used in digital soil mapping (DSM). Because the selection of proper terrain covariates relies largely on users' DSM knowledge and application context, automatic selection of terrain covariates is valuable for DSM users (especially non‐experts). Case‐based reasoning provides a promising solution to this demand. Recently, two case‐based reasoning strategies have been proposed (i.e., the covariate‐classification strategy and the most‐similar‐case strategy). However, there is no comparison of their performance, as well as that of the implemented methods based on the same or different strategies. This study fills this knowledge gap through a comparison experiment on the two representative methods for each strategy, respectively. Experiments adopted a DSM case base including 191 cases, which involve 38 terrain covariates. Results from a cross‐validation and a practical DSM application showed that the random forests method adopting the covariate‐classification strategy performed best.