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Development of a real-time regional-inundation forecasting model for the inundation warning system

机译：洪水预警系统实时区域洪水预报模型的开发

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摘要

Accurate forecasts of the inundation depth are necessary for inundation warning and mitigation. In this paper, a real-time regional forecasting model is proposed to yield 1- to 3-h lead time inundation maps. First, the K-means based cluster analysis is developed to group the inundation depths and to indentify the control points. Second, the support vector machine is used as the computational method to develop the point forecasting module to yield inundation forecasts for each control point. Third, based on the forecasted depths and the geographic information, the spatial expansion module is developed to expand the point forecasts to the spatial forecasts. An actual application to Siluo Township, Taiwan, is conducted to demonstrate the advantage of the proposed model. The results indicate that the proposed model can provide accurate inundation maps for 1- to 3-h lead times. The accurate long lead time forecasts can extend the lead time to allow sufficient time to take emergency measures. Furthermore, the proposed model is an efficient process that can be trained rapidly with real-time data and is more suitable to be integrated with the decision support system. In conclusion, the proposed modeling technique is expected to be useful to support the inundation warning systems.
机译：对淹没深度的准确预测对于淹没预警和缓解是必要的。本文提出了一种实时区域预报模型，以产生1-3小时的提前期淹没图。首先，开发了基于K均值的聚类分析以对淹没深度进行分组并确定控制点。其次，使用支持向量机作为计算方法来开发点预测模块，以产生每个控制点的淹没预测。第三，基于预测的深度和地理信息，开发空间扩展模块以将点预测扩展为空间预测。进行了台湾四罗乡的实际应用，以证明该模型的优势。结果表明，提出的模型可以为1至3小时的交货期提供准确的淹没图。准确的长交货时间预测可以延长交货时间，以便有足够的时间采取紧急措施。此外，所提出的模型是一种高效的过程，可以使用实时数据快速进行训练，并且更适合与决策支持系统集成。总之，所提出的建模技术有望对支持洪水预警系统有用。

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