用深度学习替代传统洪水模拟，快速预测水位分布。
A Deep U-Net Framework for Flood Hazard Mapping Using Hydraulic Simulations of the Wupper Catchment
- 构建U-Net深度模型，通过优化结构与数据处理提升预测精度。
- 在德国威珀流域测试，结果与真实模拟相当，计算速度显著提升。
- 适合需要实时洪水预警的水利部门或应急管理部门使用。
全球洪水事件频发且日益严重，亟需快速可靠的洪水预测工具。传统方法依赖计算成本高昂的水力模拟。本研究通过构建基于深度学习的代理模型，高效准确地预测网格上的最大水位。通过一系列实验优化了U-Net架构、图像块生成与数据处理策略，以逼近水力模型。研究证明，深度学习代理模型可作为传统水力模拟的高效替代方案。该框架在德国北莱茵-威斯特法伦州威珀流域的水力模拟中进行了验证，结果表现相当。
原文摘要 · Abstract (English)
The increasing frequency and severity of global flood events highlights the need for the development of rapid and reliable flood prediction tools. This process traditionally relies on computationally expensive hydraulic simulations. This research presents a prediction tool by developing a deep-learning based surrogate model to accurately and efficiently predict the maximum water level across a grid. This was achieved by conducting a series of experiments to optimize a U-Net architecture, patch generation, and data handling for approximating a hydraulic model. This research demonstrates that a deep learning surrogate model can serve as a computationally efficient alternative to traditional hydraulic simulations. The framework was tested using hydraulic simulations of the Wupper catchment in the North-Rhein Westphalia region (Germany), obtaining comparable results.
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