基于动力系统理论的实用数据驱动洪水预报：日本案例研究
Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan
摘要：
数据驱动的洪水预报方法是有用的，特别是对于缺乏水文信息的河流建立物理模型。虽然这些以前的方法可以预测河流水位只使用过去的水位和降雨量数据，他们不能处理以前没有经验的水位容易，并需要大量的数据来建立准确的模型。在这里，我们专注于相空间重构方法，并开发一个实用的数据驱动的预测方法，以克服现有的问题。所提出的方法可以处理未经历的水位，并提供预测，仅使用少量的水上升事件。将该方法应用于实际河流的数据，在现有的方法中，包括物理径流模型，数据驱动的多层感知器和基于相空间重构的传统方法，它取得了最好的预测性能。此外，该方法还能提前6 h预测出陡河的疏散警戒水位。鉴于其性能和可维护性，所提出的方法可以应用于许多实际河流的早期疏散。
英文摘要：
Data-driven flood forecasting methods are useful, especially for the rivers that lack hydrological information to build physical models. Although these former methods can forecast river stages using only past water levels and rainfall data, they cannot handle previously unexperienced water levels easily, and require a large amount of data to build accurate models. Here, we focus on phase-space reconstruction approaches, and develop a practical data-driven forecasting method to overcome the existing problems. The proposed method can handle the unexperienced water levels and provide forecasts using only a small number of water rise events. We apply the method to data from actual rivers, and it achieved the best forecast performance among existing methods, including a physical runoff model, a data-driven multi-layer perceptron, and a conventional method based on phase-space reconstruction. In addition, the proposed method also forecasted the exceedance of the evacuation warning level 6 h earlier for steep rivers. Given its performance and maintainability, the proposed method can be applied to many actual rivers for early evacuation.
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