Abstract Flood inundation models are important tools in flood management. Commonly used flood inundation models, such as hydrodynamic or simplified conceptual models, are either computationally intensive or cannot simulate the temporal behavior of floods. Therefore, emulation models based on data-driven methods, such as artificial neural networks (ANNs), have been developed. However, the performance of ANN models, like any other data-driven models, is limited by available data and will not perform well in data-sparse regions. In this study, we developed an ANN-based hybrid modeling approach to improve model performance in data-sparse regions by leveraging better model performance in data-rich regions. We applied our proposed hybrid modeling approach with three ANN models, including the traditional point-based ANN and two newly proposed block-based ANN models. The results demonstrate that all three ANN models have better performance in data-rich regions compared to data-sparse regions as expected, with the block-based ANN with the most complicated model structure performing better in data-rich regions and the simplest point-based ANN performing better in data-sparse regions. The hybrid modeling approach can significantly improve model performance in data-sparse regions, with the hybrid model based on the most complex block-based ANN performing the best. Our results show the importance of considering the trade-offs between data availability and model complexity in developing data-driven models, and demonstrate the potential for improving performance in data-sparse regions by using a hybrid modeling approach that optimizes model complexity based on data availability.

中文翻译：

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基于人工神经网络的洪水淹没建模混合建模方法

摘要 洪水淹没模型是洪水管理的重要工具。常用的洪水淹没模型，例如水动力模型或简化的概念模型，要么计算量大，要么无法模拟洪水的时间行为。因此，已经开发了基于数据驱动方法的仿真模型，例如人工神经网络 (ANN)。然而，与任何其他数据驱动模型一样，ANN 模型的性能受到可用数据的限制，并且在数据稀疏区域中表现不佳。在这项研究中，我们开发了一种基于 ANN 的混合建模方法，通过利用数据丰富区域中更好的模型性能来提高数据稀疏区域中的模型性能。我们将我们提出的混合建模方法应用于三个 ANN 模型，包括传统的基于点的 ANN 和两个新提出的基于块的 ANN 模型。结果表明，与预期的数据稀疏区域相比，所有三个 ANN 模型在数据丰富区域都具有更好的性能，具有最复杂模型结构的基于块的 ANN 在数据丰富区域和最简单的基于点的区域中表现更好。 ANN 在数据稀疏区域表现更好。混合建模方法可以显着提高数据稀疏区域的模型性能，其中基于最复杂的基于块的 ANN 的混合模型性能最好。我们的结果表明在开发数据驱动模型时考虑数据可用性和模型复杂性之间的权衡的重要性，