Abstract Case revision has always been challenging in case-based reasoning (CBR) processes. Most CBR methods used for analyzing geological disasters fail to consider the spatial relationships among geological environmental factors. Therefore, conventional case revision rules do not allow for effective case-based reasoning for geologic disaster assessment. In this study, we first establish a spatial case library of historical geological disasters. Subsequently, these spatial cases are organized, reduced, and weighted using spatial clustering, genetic algorithm and rough set hybrid algorithms, respectively. Based on these efforts, we propose a retrieval method for the spatial cases and evaluate the proposed solution for a target case. Finally, a geological disaster assessment model possessing a spatial case revision function is obtained by combining geographic information system (GIS) technology with the genetic algorithm. Experiments and applications show that our spatial revision approach is effective and that it exhibits a higher classification performance compared to traditional data mining methods with regard to geological disaster assessment. The approach also exhibits a higher accuracy and efficiency than typical spatial CBR or case revision models do. The results of this study thus facilitate rapid geological disaster assessment, making it more facile and convenient for decision makers to execute decisions efficiently and quickly.

中文翻译：

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地质灾害风险评估案例推理中的空间案例修正

摘要 案例修订在基于案例的推理 (CBR) 过程中一直具有挑战性。大多数用于分析地质灾害的 CBR 方法没有考虑地质环境因素之间的空间关系。因此，传统的案例修订规则不允许对地质灾害评估进行有效的基于案例的推理。本研究首先建立了历史地质灾害空间案例库。随后，分别使用空间聚类、遗传算法和粗糙集混合算法对这些空间案例进行组织、减少和加权。基于这些努力，我们提出了一种空间案例的检索方法，并针对目标案例评估了所提出的解决方案。最后，将地理信息系统(GIS)技术与遗传算法相结合，得到具有空间案例修正功能的地质灾害评估模型。实验和应用表明，我们的空间修正方法是有效的，与传统的数据挖掘方法相比，它在地质灾害评估方面表现出更高的分类性能。该方法还表现出比典型的空间 CBR 或案例修订模型更高的准确性和效率。因此，这项研究的结果有助于快速进行地质灾害评估，使决策者更轻松、更方便地高效、快速地执行决策。实验和应用表明，我们的空间修正方法是有效的，与传统的数据挖掘方法相比，它在地质灾害评估方面表现出更高的分类性能。该方法还表现出比典型的空间 CBR 或案例修订模型更高的准确性和效率。因此，这项研究的结果有助于快速进行地质灾害评估，使决策者更轻松、更方便地高效、快速地执行决策。实验和应用表明，我们的空间修正方法是有效的，与传统的数据挖掘方法相比，它在地质灾害评估方面表现出更高的分类性能。该方法还表现出比典型的空间 CBR 或案例修订模型更高的准确性和效率。因此，这项研究的结果有助于快速进行地质灾害评估，使决策者更轻松、更方便地高效、快速地执行决策。