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An adaptive predictive approach for river level forecasting

机译:河流水位预测的自适应预测方法

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Efficient flood management requires accurate real time forecasts to allow early warnings, real time control of hydraulics structures or other actions. Commercially available computing tools typically use, for flow or level forecasting, hydraulic models derived from the numerical approximation of Saint-Venant equations. These tools need powerful computers, accurate knowledge of the riverbed topography and skilled operators with some hydraulic background. This paper presents an alternative approach in which the river network is modeled as a cascade of interconnected input-output systems. Each system is modeled by an adaptive predictive expert model, which provides real-time fast and accurate forecasts over a moving prediction horizon. The main advantages of the approach are: (1) simplicity in the formulation and low computational burden; (2) no need of topographic information on the river waterbeds; (3) operators do not need strong hydraulic knowledge and the forecast may be done autonomously. The approach is evaluated using real data from the Ebro river basin in Spain.
机译:高效的洪水管理需要准确的实时预测,以进行预警,对水力结构进行实时控制或采取其他措施。对于流量或液位预测,可商购获得的计算工具通常使用从Saint-Venant方程的数值近似推导出的水力模型。这些工具需要强大的计算机,对河床地形的准确了解以及具有一定液压背景的熟练操作员。本文提出了一种替代方法,其中将河网建模为相互连接的输入-输出系统的级联。每个系统均由自适应预测专家模型建模,该模型可在移动的预测范围内提供实时,快速,准确的预测。该方法的主要优点是:(1)公式简单,计算量小; (2)不需要河水床的地形信息; (3)操作员不需要强大的水力知识,并且可以自动进行预测。该方法是使用来自西班牙埃布罗河流域的真实数据进行评估的。

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