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首页> 外文期刊>Soil Science Society of America Journal >Integrating Different Types of Knowledge for Digital Soil Mapping
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Integrating Different Types of Knowledge for Digital Soil Mapping

机译：整合不同类型的知识以进行数字土壤制图

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摘要

Analysis of the soil scientists' knowledge provides guidelines for the development of knowledge-based digital soil mapping (DSM) methodologies and software tools. Literature addressing the analysis and integration of different types of soil scientists' knowledge is limited. We analyze the knowledge from the perspectives of scale and space. We distinguish global knowledge that covers the entire mapping area and local knowledge that is only applicable to certain local regions. We also distinguish knowledge represented by environmental values in parametrical space and knowledge represented by locations in geographical space. Rule-based reasoning (RBR) is proposed for handling the global knowledge in parametrical space, global case-based reasoning (CBR) for the global knowledge in geographical space, and local CBR for the local knowledge in geographical space. The final soil mapping products should represent an integration of knowledge and inferences of all different types. A software tool, named Soil Inference Engine (SIE), was developed to facilitate an eight-step integrated RBR-CBR DSM process. The SIE was tested in a pilot project in northern Vermont and proved to be effective. The soil scientist working on the project was generally satisfied with the results from SIE, in terms of both quality and cost.
机译：对土壤科学家知识的分析为开发基于知识的数字土壤测绘（DSM）方法和软件工具提供了指导。涉及分析和整合不同类型土壤科学家知识的文献非常有限。我们从规模和空间的角度分析知识。我们区分了覆盖整个地图区域的全局知识和仅适用于某些局部区域的局部知识。我们还将参数空间中以环境价值表示的知识与地理空间中以位置表示的知识区分开。提出了基于规则的推理（RBR），用于处理参数空间中的全局知识，针对地理空间中的全局知识来处理基于全局案例的推理（CBR），以及针对地理空间中的本地知识来进行局部CBR。最终的土壤测绘产品应代表所有不同类型知识和推理的整合。开发了名为“土壤推断引擎”（SIE）的软件工具，以促进八步集成的RBR-CBR DSM过程。 SIE在佛蒙特州北部的一个试点项目中进行了测试，并证明是有效的。就质量和成本而言，从事该项目的土壤科学家对SIE的结果普遍感到满意。

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