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An adaptive predictive approach for river level forecasting

机译:河流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,以允许早期警告,实时控制液压结构或其他动作。商业上可获得的计算工具通常使用,用于流量或水平预测,源自Saint-Vent方程的数值逼近的液压模型。这些工具需要强大的计算机,准确了解河床地形和技术操作员,其中一些液压背景。本文介绍了一种替代方法,其中河网络被建模为互连的输入输出系统的级联。每个系统都由自适应预测专家模型建模,其在移动预测地平线上提供实时快速准确的预测。该方法的主要优点是:(1)在配方和低计算负担中的简单性; (2)无需河水床的地形信息; (3)运营商不需要强大的液压知识,并且预测可能是自主的。使用来自西班牙埃布罗河流域的真实数据来评估该方法。

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