A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction
Yi-Jie Wang,
Cheng-Zhi Qin,
Peng Liang,
Liang-Jun Zhu,
Zi-Yue Chen,
Cheng-Long Wu,
A-Xing Zhu
Affiliations
Yi-Jie Wang
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
Cheng-Zhi Qin
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
Peng Liang
Key Laboratory of Earthquake Prediction, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China
Liang-Jun Zhu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
Zi-Yue Chen
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
Cheng-Long Wu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
A-Xing Zhu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
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 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.
