This paper presents a purely data-driven deep-learning approach for flood maps forecasting. For the first time in this context a Transformer-based algorithm is employed to address one of the main issues in early-warning systems for flood propagation, i.e., the long computational times required to forecast the inundation evolution in real time. The proposed model, named “FloodSformer”, is trained to extract the spatiotemporal information from a short sequence of water depth maps and predict the water depth map at one subsequent instant. Then, to forecast a sequence of future maps, we employ an autoregressive procedure based on the trained surrogate model. The method was applied to both synthetic dam-break scenarios and to a real case study, specifically the ideal failure of the Parma River dam (Italy). The training and testing datasets were generated numerically from two-dimensional hydraulic simulations. In the case of the real test case, the average Root Mean Square Error was found to be equal to 10.4 cm. The short computational time (e.g., the forecast of 90 maps, representing a lead time of 3 h, takes less than 1 min) makes the FloodSformer model a suitable tool for real-time emergency applications.

中文翻译：

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使用基于变压器的深度学习模型对溃坝场景进行实时洪水地图预测

本文提出了一种用于洪水地图预测的纯粹数据驱动的深度学习方法。在此背景下，首次采用基于 Transformer 的算法来解决洪水传播预警系统的主要问题之一，即实时预测洪水演变所需的较长计算时间。所提出的模型名为“FloodSformer”，经过训练，可以从短序列的水深图中提取时空信息，并预测后续时刻的水深图。然后，为了预测一系列未来地图，我们采用基于训练后的代理模型的自回归程序。该方法适用于合成溃坝情景和真实案例研究，特别是帕尔马河大坝（意大利）的理想溃坝。训练和测试数据集是通过二维水力模拟以数字方式生成的。在实际测试案例中，平均均方根误差等于 10.4 厘米。较短的计算时间（例如，90 张地图的预测，相当于 3 小时的提前时间，只需不到 1 分钟）使得 FloodSformer 模型成为实时应急应用的合适工具。