A disaster-severity assessment DSS comparative analysis
A disaster-severity assessment DSS comparative analysis
复制标题
灾害严重程度评估DSS比较分析
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
V. Kecman
中科院分区：
文献类型：
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作者：
Juan Tinguaro Rodríguez;B. Vitoriano;J. Montero;V. Kecman
本文以自然灾害严重程度评估决策支持系统(DSS)的开发为背景，对基于模糊规则的系统和一些标准的统计及其他机器学习技术进行了比较分析。这一决策支持系统将被称为战略决策支持系统，由提交人提出，目的是帮助那些非政府组织内部的决策者设计和执行国际人道主义救灾行动。通过一套关于灾难情景的一套容易获取的信息和关于类似灾难情景的历史数据，SEDD能够对几乎每一种潜在灾难情景的后果进行相对准确和可解释的评估。因此，虽然经济、社会和文化发展部的方法相当复杂，但其数据要求很小，因此，可以将其用于需要人道主义援助的非政府组织和国家。从这个意义上说，SEDD反对目前的一些工具，这些工具只关注一个现象--一个地方的灾难情景(加利福尼亚州的地震、佛罗里达州的飓风等)。和/或具有广泛和/或技术复杂的数据要求(实时遥感信息、详尽的建筑物普查等)。此外，虽然侧重于灾害应对，但可持续发展战略也可用于灾害管理的其他阶段，如减灾或备灾。特别是，本文将SEDD模糊方法与多元线性回归、线性判别分析、分类树和支持向量机在灾情评估中的预测精度和可解释性进行了比较。在对EM-DAT灾害数据库进行广泛验证之后，得出的结论是，在同时为评估灾害后果提供一个准确和可解释的推理工具的任务中，SEDD优于上述方法。
This paper aims to provide a comparative analysis of fuzzy rule-based systems and some standard statistical and other machine learning techniques in the context of the development of a decision support system (DSS) for the assessment of the severity of natural disasters. This DSS, which will be referred to as SEDD, has been proposed by the authors to help decision makers inside those Non-Governmental Organizations (NGOs) concerned with the design and implementation of international operations of humanitarian response to disasters. SEDD enables a relatively highly accurate and interpretable assessment on the consequences of almost every potential disaster scenario to be obtained through a set of easily accessible information about that disaster scenario and historical data about similar ones. Thus, although SEDD’s methodology is rather sophisticated, its data requirements are small, which, therefore, enables its use in the context of NGOs and countries requiring humanitarian aid. In this sense, SEDD opposes to some current tools which focuses on one phenomena-one place disaster scenarios (earthquakes in California, hurricanes in Florida, etc.) and/or have extensive and/or technologically sophisticated data requirements (real-time remote sensing information, exhaustive building census, etc.). Moreover, although focused on disaster response, SEDD can also be useful in other phases of disaster management, as disaster mitigation or preparedness. Particularly, the predictive accuracy and interpretability of SEDD fuzzy methodology is compared here in a disaster severity assessment context with those of multiple linear regression, linear discriminant analysis, classification trees and support vector machines. After an extensive validation over the EM-DAT disaster database, it is concluded that SEDD outperforms the methods above in the task of simultaneously providing an accurate and interpretable inference tool for the evaluation of the consequences of disasters.