利用大数据分析的机会,更准确地预测自然灾害的社会后果

IF 1.7 3区 工程技术 Q3 ENGINEERING, CIVIL
Jessica Boakye, P. Gardoni, C. Murphy
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引用次数: 13

摘要

由于技术和数据共享的进步,数据源的可用性大大增加。有了这些新的数据源和更大的数据量,工程师们就有了一个独特的机会来创建更真实、信息更丰富的模型,这些模型可以在现实世界的应用中使用。本文提出了一个概率框架,用于使用大数据来评估和预测个人在灾害发生之前和之后的福祉。数据被用于能力方法(CA),其中能力被定义为幸福的重要维度,反映了个人有真正的机会去做或成为什么。本文还讨论了大数据带来的三大挑战:隐私、来源有效性和准确性。作为一个例子,概率框架被用于研究沿海社区家庭在假设地震发生后的庇护能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using opportunities in big data analytics to more accurately predict societal consequences of natural disasters
ABSTRACT The availability of data sources has greatly increased due to advances in technology and data sharing. With these new data sources and significantly larger volume of data, engineers have been presented with a unique opportunity to create more realistic and informative models that can be used in real world applications. This paper presents a probabilistic framework for using big data to assess and predict the well-being of individuals before and in the aftermath of a hazard. Data are used to inform a Capability Approach (CA) where capabilities are defined as important dimensions of well-being reflecting what individuals have a genuine opportunity to do or become. The paper also addresses three of the grand challenges presented by big data: privacy, source validity, and accuracy. As an example, the probabilistic framework is used to study the ability of households in a coastal community to be sheltered in the aftermath of a hypothetical earthquake.
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来源期刊
Civil Engineering and Environmental Systems
Civil Engineering and Environmental Systems 工程技术-工程:土木
CiteScore
3.30
自引率
16.70%
发文量
10
审稿时长
>12 weeks
期刊介绍: Civil Engineering and Environmental Systems is devoted to the advancement of systems thinking and systems techniques throughout systems engineering, environmental engineering decision-making, and engineering management. We do this by publishing the practical applications and developments of "hard" and "soft" systems techniques and thinking. Submissions that allow for better analysis of civil engineering and environmental systems might look at: -Civil Engineering optimization -Risk assessment in engineering -Civil engineering decision analysis -System identification in engineering -Civil engineering numerical simulation -Uncertainty modelling in engineering -Qualitative modelling of complex engineering systems
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