On the use of Bayesian decision theory for issuing natural hazard warnings.
On the use of Bayesian decision theory for issuing natural hazard warnings.
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DOI：
10.1098/rspa.2016.0295
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Mylne KR
中科院分区：
文献类型：
--
作者：
Economou T;Stephenson DB;Rougier JC;Neal RA;Mylne KR
预防自然灾害可提高社会的复原力，是在不确定情况下作出决策的一个很好的例子。预警系统只有在定义明确并为利益攸关方所理解的情况下才是有用的。然而，大多数业务预警系统是启发式的：没有正式或透明的定义。贝叶斯决策理论提供了一个框架，在不确定的情况下发出警告，但尚未得到充分利用。在这里，提出了一个决策理论框架的危险警告。该框架允许任何数量的警告级别和未来的自然状态，并描述了一个数学模型，为通用和特定的最终用户构建必要的损失函数。该方法说明了使用一天提前警告的每日严重降水在英国，并比较目前的决策工具，由英国气象局。提出了一个概率模型来预测降水，集合预报信息，损失函数构造两个通用的利益相关者：最终用户和预报员。结果表明，英国气象局的工具发出更少的高级别的警告相比，我们的系统的通用最终用户，这表明前者可能不适合风险厌恶的最终用户。此外，原始集合预报被证明是不可靠的，并导致更高的损失警报。
Warnings for natural hazards improve societal resilience and are a good example of decision-making under uncertainty. A warning system is only useful if well defined and thus understood by stakeholders. However, most operational warning systems are heuristic: not formally or transparently defined. Bayesian decision theory provides a framework for issuing warnings under uncertainty but has not been fully exploited. Here, a decision theoretic framework is proposed for hazard warnings. The framework allows any number of warning levels and future states of nature, and a mathematical model for constructing the necessary loss functions for both generic and specific end-users is described. The approach is illustrated using one-day ahead warnings of daily severe precipitation over the UK, and compared to the current decision tool used by the UK Met Office. A probability model is proposed to predict precipitation, given ensemble forecast information, and loss functions are constructed for two generic stakeholders: an end-user and a forecaster. Results show that the Met Office tool issues fewer high-level warnings compared with our system for the generic end-user, suggesting the former may not be suitable for risk averse end-users. In addition, raw ensemble forecasts are shown to be unreliable and result in higher losses from warnings.
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影响因子：
2.7
作者：
Mylne, KR
通讯作者：
Mylne, KR
影响因子：
2.7
作者：
Neal, Robert A.;Boyle, Patricia;Sharpe, Michael
通讯作者：
Sharpe, Michael
影响因子：
2.5
作者：
Dow, K;Cutter, SL
通讯作者：
Cutter, SL
影响因子：
5.4
作者：
KRZYSZTOFOWICZ, R
通讯作者：
KRZYSZTOFOWICZ, R
影响因子：
6.3
作者：
Martina, M. L. V.;Todini, E.;Libralon, A.
通讯作者：
Libralon, A.