ABSTRACTSpatial 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 KARITE HYDRA HYDRATING SHINE SHAMPOO 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 Womens Tee leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies.