Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

中文翻译：

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使用可解释的 CNN 模型提高数据驱动的城市洪水预测的透明度


在人口和财产密集的城市地区减轻洪水造成的严重损失，需要对应急措施进行有效的洪水预测。基于物理的模型面临实时洪水预测计算效率低的问题。快速预测而不是基于物理的模型的另一种方法是从数据驱动的角度进行预测。然而，用于城市洪水预测的数据驱动方法通常被视为“黑匣子”，可能会引起担忧。在这项研究中，我们提出了一种可解释的深度学习 （DL） 方法，用于使用卷积神经网络 （CNN） 和可解释的人工智能 （AI） 框架 Shapley 加法解释 （SHAP） 以增强透明度进行快速城市洪水预测。我们处理一个系统的逐步特征选择过程，并建立一个考虑地形、排水网络和降雨的 CNN 模型，以预测最大洪水深度。然后，应用 SHAP 为模型结果中的决策提供可信的解释。结果表明：1） 前向选择可以为选择有效的输入变量以改进预测提供见解，并促进对 DL 建模的理解。所提出的 CNN 模型预测的淹没深度空间模式与基于物理的模型预测的空间模式具有良好的一致性，平均相关系数 （CC） 和平均绝对误差 （MAE） 值分别为 0.982 和 0.021 m。2） 当使用相同的硬件时，CNN 模型在计算速度上大大优于基于物理的模型，在案例研究中，GPU 加速 210 倍，CPU 加速 51 倍（575167网格单元，14.38 km2）。 特别是，它仍然可以在没有高性能硬件的纯 CPU 标准笔记本电脑上实现良好的性能，计算时间仅略微增加。3） SHAP 可解释分析证实 CNN 模型正确捕获了输入变量与水深之间的关系，揭示了合理的决策过程，提高了其透明度。结合 SHAP 进行城市洪水快速预测的可解释 DL 方法有望在利益相关者之间建立信任，并为在洪水紧急情况下旨在挽救生命和保护资产的迅速措施提供值得信赖的参考。此外，所提出的 DL 方法有可能进一步扩展以分析城市洪水事件的原因，并作为探索数据驱动的城市洪水预测的可转移性的基础，为更好的城市洪水风险管理提供好处。