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首页> 外文期刊>Journal of Hydrology >Artificial neural network based hybrid modeling approach for flood inundation modeling
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Artificial neural network based hybrid modeling approach for flood inundation modeling

机译：基于人工神经网络的洪水淹没建模混合模拟方法

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摘要

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 datadriven 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 datasparse 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 datadriven 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.
机译：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，如水动力或简化概念模型，要么计算密集，要么无法模拟洪水的时间行为。因此，基于数据驱动方法的仿真模型，如人工神经网络（ANN）已经被开发出来。然而，与任何其他数据驱动的模型一样，ANN模型的性能受到可用数据的限制，在数据稀疏区域表现不佳。在本研究中，我们开发了一种基于人工神经网络的混合建模方法，通过在数据丰富的区域利用更好的模型性能来提高数据稀疏区域的模型性能。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，这三种神经网络模型在数据丰富区域的性能都更好，具有最复杂模型结构的块神经网络在数据丰富区域的性能更好，而最简单的基于点的神经网络在数据解析区域的性能更好。混合建模方法可以显著提高数据稀疏区域的模型性能，基于最复杂块的人工神经网络的混合模型性能最好。我们的结果显示了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，并展示了通过使用基于数据可用性优化模型复杂性的混合建模方法来提高数据稀疏区域性能的潜力。

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