The rise in urban flooding events poses a threat to public safety, property, and economic stability. To prevent urban flooding and manage stormwater effectively, relying solely on engineering solutions is insufficient. Therefore, it is critical to implement non-engineering measures such as urban flood warnings and forecasting. This article reviews the characteristics of different urban flood models based on different hydrological and hydrodynamic principles and deep learning (DL). It highlights the limitations of coupled hydrological-hydrodynamic models in terms of timeliness. Additionally, it discusses research on the use of Numerical Simulation in hydrological early warning and forecasting. Compared to traditional hydrodynamic models that rely on physical mechanisms, models driven by DL methods can effectively and adaptively extract input-output relationships of complex systems. Subsequently, a summary of the current flood models is presented, followed by a discussion of future development trends and challenges.

中文翻译：

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城市洪水模拟研究进展与展望：从传统数值模型到深度学习方法


城市洪水事件的增加对公共安全、财产和经济稳定构成威胁。为了有效防止城市洪水和管理雨水，仅仅依靠工程解决方案是不够的。因此，实施城市洪水预警预报等非工程措施至关重要。本文回顾了基于不同水文学和水动力原理以及深度学习（DL）的不同城市洪水模型的特点。它强调了耦合水文-水动力模型在时效性方面的局限性。此外，还讨论了数值模拟在水文预警和预报中的应用研究。与依赖物理机制的传统流体动力学模型相比，深度学习方法驱动的模型可以有效地、自适应地提取复杂系统的输入输出关系。随后，总结了当前的洪水模型，并讨论了未来的发展趋势和挑战。