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Using the most similar case method to automatically select environmental covariates for predictive mapping
Earth Science Informatics ( IF 2.7 ) Pub Date : 2020-05-02 , DOI: 10.1007/s12145-020-00466-5
Peng Liang , Cheng-Zhi Qin , A-Xing Zhu , Tong-Xin Zhu , Nai-Qing Fan , Zhi-Wei Hou

Predictive mapping of environment is an important means for environment assessment and management. The selection of predictor variables (or environmental covariates) is the first and key step in predictive mapping. A number of machine learning and statistical models have been developed to select what and how many environmental covariates in a wide range of predictive mapping. Nevertheless, those models require a large amount of field data for model training and calibration, which can be problematic in applying to the areas with no or very limited field data available. To overcome the shortcoming, this paper proposes the most similar case method for selecting environmental covariates for predictive mapping. First, we describe the basic idea and the development procedures of the most similar case method; second, as an experimental test, we employ the proposed method to select the topographic covariates for inputting to the predictive soil mapping; third, we evaluate the effectiveness of the proposed method in the designed experiment using the leave-one-out cross-validation method. In total, 191 evaluation cases are included in the experimental case base and the test results show that 58.7% of the topographic covariates originally used in each evaluation case are correctly selected by the proposed method, which suggests that the proposed most-similar-case method perform reasonably well even with a relatively limited size of the case base. The future work should include the selection of other types of environmental covariates (e.g., climate, organism, etc.) and the development of an automatic method to extract the existing application cases from literature.

中文翻译:

使用最相似的案例方法自动选择环境协变量以进行预测映射

预测性环境绘图是进行环境评估和管理的重要手段。预测变量(或环境协变量)的选择是预测映射的第一步和关键步骤。已经开发了许多机器学习和统计模型,以在广泛的预测映射中选择什么和多少环境协变量。然而,这些模型需要大量的现场数据来进行模型训练和校准,这在应用于没有可用现场数据或现场数据非常有限的区域时可能会出现问题。为了克服该缺点,本文提出了最相似的案例方法来选择环境协变量进行预测性映射。首先,我们描述最相似案例方法的基本思想和开发过程;其次,作为实验测试 我们采用建议的方法来选择地形协变量,以输入到预测性土壤测绘中。第三,我们使用留一法交叉验证方法在设计的实验中评估了该方法的有效性。实验案例库中总共包含191个评估案例,测试结果表明,所提出的方法正确选择了每个评估案例中最初使用的地形协变量的58.7%,这表明所提出的最相似案例方法即使案例库的大小相对有限,也可以表现良好。未来的工作应包括选择其他类型的环境协变量(例如,气候,生物等),以及开发一种自动方法以从文献中提取现有应用案例的方法。
更新日期:2020-05-02
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