In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water depth prediction are nowadays important as urban floods are increasing due to higher rainfall intensity caused by climate change, expansion of cities and changes in land use. While hydrodynamic models models can provide reliable forecasts by simulating water depth at every location of a catchment, they also have a high computational burden which jeopardizes their application to real-time prediction in large urban areas at high spatial resolution. Here, we propose to address this issue by using data-driven techniques. Specifically, we evaluate deep learning models which are trained to reproduce the data simulated by the CADDIES cellular-automata flood model, providing flood forecasts that can occur at different future time horizons. The advantage of using such models is that they can learn the underlying physical phenomena a priori, preventing manual parameter setting and computational burden. We perform experiments on a dataset consisting of two catchments areas within Switzerland with 18 simpler, short rainfall patterns and 4 long, more complex ones. Our results show that the deep learning models present in general lower errors compared to the other methods, especially for water depths $>0.5m$. However, when testing on more complex rainfall events or unseen catchment areas, the deep models do not show benefits over the simpler ones.

翻译：本技术报告比较了不同深度学习模型在高空间分辨率下预测水深栅格的能力。由于气候变化导致降雨强度增加、城市扩张及土地利用变化，城市洪水日益频发，因此高效、精确且快速的水深预测方法在当前具有重要意义。尽管水动力学模型可通过模拟流域内每个位置的水深提供可靠预报，但其高计算负担限制了其在高空间分辨率下对大城市区域的实时预测应用。针对此问题，我们提出采用数据驱动技术。具体而言，我们评估了经过训练的深度学习模型，这些模型可复现由CADDIES元胞自动机洪水模型模拟的数据，从而提供不同未来时间尺度的洪水预报。此类模型的优势在于能先验地学习潜在物理现象，避免了手动参数设置和计算负担。我们在包含瑞士两个流域的数据集上进行了实验，该数据集包含18个简单短时降雨模式及4个复杂长时降雨模式。结果表明：与其他方法相比，深度学习模型整体误差更低，尤其在水深>0.5米时表现更优。然而，当在更复杂降雨事件或未见流域进行测试时，深度学习模型未展现出优于简单模型的优势。