用轻量CNN模型预测海平面上升下沿海城市洪涝，精度提升近20%。
Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning
- 基于视觉的轻量CNN模型，支持不同海平面上升与岸线适应场景
- 在阿布扎比和旧金山数据集上平均降低20%洪水深度预测误差
- 适合城市规划者快速评估气候变化下的防洪策略
气候变化与海平面上升对沿海城市构成日益严峻的威胁，亟需高效精准的洪涝预测方法。传统物理驱动水动力模拟器虽精确，但计算成本高，难以用于城市尺度的海岸规划。深度学习技术提供可行替代方案，但常受限于数据稀缺和高维输出需求。本文基于一种新型视觉导向、低资源的深度学习框架，构建了一种轻量级卷积神经网络（CNN）模型，可预测不同海平面上升投影及岸线适应情景下的海岸洪涝。通过使用阿布扎比与旧金山两个不同区域的数据集，验证了模型在跨地理场景中的泛化能力。结果表明，该模型相比现有先进方法，平均将洪水深度图预测的均方绝对误差（MAE）降低约20%。研究凸显了该方法在海岸洪涝管理中作为可扩展、实用工具的潜力，助力决策者应对气候变化带来的挑战。
原文摘要 · Abstract (English)
Climate change and sea-level rise (SLR) pose escalating threats to coastal cities, intensifying the need for efficient and accurate methods to predict potential flood hazards. Traditional physics-based hydrodynamic simulators, although precise, are computationally expensive and impractical for city-scale coastal planning applications. Deep Learning (DL) techniques offer promising alternatives, however, they are often constrained by challenges such as data scarcity and high-dimensional output requirements. Leveraging a recently proposed vision-based, low-resource DL framework, we develop a novel, lightweight Convolutional Neural Network (CNN)-based model designed to predict coastal flooding under variable SLR projections and shoreline adaptation scenarios. Furthermore, we demonstrate the ability of the model to generalize across diverse geographical contexts by utilizing datasets from two distinct regions: Abu Dhabi and San Francisco. Our findings demonstrate that the proposed model significantly outperforms state-of-the-art methods, reducing the mean absolute error (MAE) in predicted flood depth maps on average by nearly 20%. These results highlight the potential of our approach to serve as a scalable and practical tool for coastal flood management, empowering decision-makers to develop effective mitigation strategies in response to the growing impacts of climate change. Project Page: https://caspiannet.github.io/
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