Innovative Approaches for Geometric Uncertainty Quantification in an Operational Oil Spill Modeling System
Innovative Approaches for Geometric Uncertainty Quantification in an Operational Oil Spill Modeling System
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DOI：
10.3390/jmse7080259
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发表时间：
2019-08-01
影响因子：
2.9
通讯作者：
Hodges, Ben R.
中科院分区：
文献类型：
--
作者：
Feng, Dongyu;Passalacqua, Paola;Hodges, Ben R.
可靠和快速的实时预测可能的石油运输路径对于应急响应管理人员的决策和泄漏后的及时清理至关重要。由于高分辨率的流体动力学模型速度较慢，实际的溢油系统通常依赖于相对粗网格模型，以提供对不久的将来地表水速度和石油迁移路径的快速估计。然而，粗网格分辨率引入模型结构误差，这被称为“几何不确定性”。目前，应急响应管理人员没有现成的方法来估计几何不确定性如何影响预测。本研究开发了新的方法来量化几何不确定性，使用细网格和粗网格模型内的泻湖河口沿着墨西哥湾北方海岸。使用几何不确定性的措施，我们提出并测试了一个新的数据驱动的不确定性模型沿着与多模型集成的方法，以量化这种不确定性在操作方面。数据驱动的不确定性模型是从机器学习算法开发的，该算法提供了对预测置信度的先验评估。多模式集成通过与有限的细网格预测进行比较来生成集合预测。这两种方法提供了明确的信息的预期规模的建模误差引起的几何不确定性的方式适合于操作建模。
Reliable and rapid real-time prediction of likely oil transport paths is critical for decision-making from emergency response managers and timely clean-up after a spill. As high-resolution hydrodynamic models are slow, operational oil spill systems generally rely on relatively coarse-grid models to provide quick estimates of the near-future surface-water velocities and oil transport paths. However, the coarse grid resolution introduces model structural errors, which have been called "geometric uncertainty". Presently, emergency response managers do not have readily-available methods for estimating how geometric uncertainty might affect predictions. This research develops new methods to quantify geometric uncertainty using fine- and coarse-grid models within a lagoonal estuary along the coast of the northern Gulf of Mexico. Using measures of geometric uncertainty, we propose and test a new data-driven uncertainty model along with a multi-model integration approach to quantify this uncertainty in an operational context. The data-driven uncertainty model is developed from a machine learning algorithm that provides a priori assessment of the prediction's confidence degree. The multi-model integration generates ensemble predictions through comparison with limited fine-grid predictions. The two approaches provide explicit information on the expected scale of modeling errors induced by geometric uncertainty in a manner suitable for operational modeling.