...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Coastal engineering >Use of a Bayesian Network for coastal hazards, impact and disaster risk reduction assessment at a coastal barrier (Ria Formosa, Portugal)
【24h】

Use of a Bayesian Network for coastal hazards, impact and disaster risk reduction assessment at a coastal barrier (Ria Formosa, Portugal)

机译：使用贝叶斯网络进行海岸屏障的海岸灾害，影响和减少灾害风险评估（葡萄牙Ria Formosa）

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Coastal communities are threatened by the impact of severe storms that may cause significant loss or damage of property and life. The main processes causing such impacts at sandy coastlines and nearby coastal communities are storm erosion, overwash and inundation. Coastal response under present conditions and under predicted climate change has been frequently assessed on the basis of numerical models, which in turn can be also used to evaluate the effectiveness of Disaster Risk Reduction (DRR) measures to mitigate the response of the coast to the imposed conditions. However, detailed morphodynamic models are computationally expensive and not commonly used by coastal managers. The present work proposes the construction of a probabilistic Bayesian Network (BN) as a surrogate for the numerical simulations. This BN is trained with a large number of morphodynamic simulations, under a variety of storm conditions and DRR measures, in order to serve as a front-end platform for visualising, analysing and evaluating combined results of the numerical modelling. The BN introduced in an early warning system will be able to serve both, as a predictive and as a working tool to determine impacts and evaluate risk reduction after measures implementation.
机译：沿海社区受到严重风暴的威胁，可能造成重大财产损失或生命损失。在沙质海岸线和附近的沿海社区造成此类影响的主要过程是风暴侵蚀，过度冲洗和淹没。经常在数值模型的基础上评估当前条件和预计的气候变化下的海岸响应，而数值模型又可以用来评估减少灾害风险（DRR）措施的有效性，以减轻海岸对强加于地面的响应条件。但是，详细的形态动力学模型在计算上很昂贵，并且沿海管理人员并不常用。本工作提出了概率贝叶斯网络（BN）的构建作为数值模拟的替代方法。该BN在各种风暴条件和DRR措施下经过大量的形态动力学模拟训练，以充当可视化，分析和评估数值建模组合结果的前端平台。在预警系统中引入的BN既可以作为预测手段，又可以作为确定措施实施后确定影响和评估风险降低的工作工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号