• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geosciences >Technical Note on the Dynamic Changes in Kalman Gain when Updating Hydrodynamic Urban Drainage Models
【24h】

Technical Note on the Dynamic Changes in Kalman Gain when Updating Hydrodynamic Urban Drainage Models

机译：更新水动力城市排水模型时卡尔曼增益动态变化的技术说明

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To prevent online models diverging from reality they need to be updated to current conditions using observations and data assimilation techniques. A way of doing this for distributed hydrodynamic urban drainage models is to use the Ensemble Kalman Filter (EnKF), but this requires running an ensemble of models online, which is computationally demanding. This can be circumvented by calculating the Kalman gain, which is the governing matrix of the updating, offline if the gain is approximately constant in time. Here, we show in a synthetic experiment that the Kalman gain can vary by several orders of magnitude in a non-uniform and time-dynamic manner during surcharge conditions caused by backwater when updating a hydrodynamic model of a simple sewer system with the EnKF. This implies that constant gain updating is not suitable for distributed hydrodynamic urban drainage models and that the full EnKF is in fact required.
机译：为了防止在线模型偏离现实，需要使用观测和数据同化技术将它们更新为当前状态。对于分布式水动力城市排水模型，这样做的一种方法是使用Ensemble Kalman滤波器（EnKF），但这需要在线运行一组模型，这在计算上是有要求的。如果该增益在时间上近似恒定，则可以通过离线计算Kalman增益（它是更新的控制矩阵）来避免这种情况。在这里，我们通过一个综合实验表明，当用EnKF更新简单下水道系统的水动力模型时，在由回水引起的超载情况下，卡尔曼增益会以不均匀且时间动态的方式变化几个数量级。这意味着恒定增益更新不适用于分布式水动力城市排水模型，并且实际上需要完整的EnKF。

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