To seek other alternative approaches besides numerical methods, the linearized Saint Venant equations were utilized to derive the channel response functions for simulating streamflow. This study advocates developing a new approach to make the temporal distribution of response functions more flexible by introducing time-varying reference parameters which depend on both the upstream inflow and the downstream boundary conditions. Moreover, to expand the model applicability in natural channels with irregular cross-sectional shapes affected by the unsteady inflow, lateral flow, and the variation of tide level, a dynamic neural network algorithm is jointly applied to determine more appropriate time-varying hydraulic parameters which are adopted in the channel flow response functions respectively derived from upstream and downstream boundary conditions. The tidal level at the estuary and the upstream inflow discharge are simultaneously considered influential factors to determine an optimal set of reference parameters by applying a machine learning technique, and this prediction can further be provided for updating the channel response functions. The novelty of this study is to propose a complete methodology to combine channel response functions with the machine learning algorithm for ameliorating the accuracy of channel flow simulation and resolving the uncertainty of model parameters. Therefore, by using the proposed method, not only the physical mechanism of Saint Venant equations can be preserved, but also the optimal hydraulic parameters can be specified. The problem of numerical instability during channel flow routing can be eliminated by using the proposed new model and therefore the reliability of real-time flood forecasting can be reinforced.

中文翻译：

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结合动态机器学习技术建立响应边界条件扰动的时变洪水波冲量函数

为了寻求除数值方法之外的其他替代方法，利用线性化圣维南方程导出用于模拟水流的渠道响应函数。本研究主张开发一种新方法，通过引入取决于上游流入和下游边界条件的时变参考参数，使响应函数的时间分布更加灵活。此外，为了扩大模型在受非稳定入流、侧向流和潮位变化影响的不规则横截面形状的自然河道中的适用性，联合应用动态神经网络算法来确定更合适的时变水力参数，分别从上游和下游边界条件导出的通道流量响应函数中采用。将河口潮位和上游流入流量同时考虑为影响因素，通过应用机器学习技术来确定一组最佳参考参数，并且该预测可以进一步提供用于更新河道响应函数。本研究的新颖之处在于提出了一种完整的方法，将渠道响应函数与机器学习算法相结合，以提高渠道流量模拟的准确性并解决模型参数的不确定性。因此，通过使用所提出的方法，不仅可以保留圣维南方程的物理机制，而且可以指定最佳水力参数。使用所提出的新模型可以消除河道流量演算过程中的数值不稳定问题，从而增强实时洪水预报的可靠性。