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A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction.
- Source :
-
Annals of GIS . Jun2024, Vol. 30 Issue 2, p199-214. 16p. - Publication Year :
- 2024
-
Abstract
- Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CASE-based reasoning
*DIGITAL soil mapping
*FORECASTING
Subjects
Details
- Language :
- English
- ISSN :
- 19475683
- Volume :
- 30
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Annals of GIS
- Publication Type :
- Academic Journal
- Accession number :
- 176845463
- Full Text :
- https://doi.org/10.1080/19475683.2024.2324398