融合Transformer与ConvLSTM，精准预测海岸地形变化。
TopoFormer: Integrating Transformers and ConvLSTMs for Coastal Topography Prediction
- 用注意力机制捕捉长期依赖，用ConvLSTM捕获局部时序特征。
- 在36个岸线单元上实现2厘米均方误差，优于现有模型。
- 适合海岸管理、导航安全等需要高精度地形预测的场景。
本文提出一种新型混合深度学习架构TopoFormer，结合基于Transformer的编码器与卷积长短期记忆（ConvLSTM）层，用于精确预测以低潮高潮（MLWS）和低潮低潮（MLWN）为参考的海岸地形剖面。准确估算至MLWS的地形数据对海岸管理、航行安全和环境监测至关重要。利用来自威尔士海岸监测中心（WCMC）的综合性数据集，包含36个海岸调查单元的超过2000次勘测，TopoFormer解决了地形预测中的时间变异性和测量数据缺失等关键挑战。该架构独特地结合多头注意力机制与ConvLSTM层，以捕捉海滩剖面数据中固有的长程依赖与局部时序模式。在与DenseNet、1D/2D CNNs及LSTMs等先进模型的对比评估中，所有模型均表现良好，但TopoFormer实现了最低的平均绝对误差（MAE），低至2厘米，并在分布内（ID）和分布外（OOD）测试中均展现出更优的准确性。
原文摘要 · Abstract (English)
This paper presents \textit{TopoFormer}, a novel hybrid deep learning architecture that integrates transformer-based encoders with convolutional long short-term memory (ConvLSTM) layers for the precise prediction of topographic beach profiles referenced to elevation datums, with a particular focus on Mean Low Water Springs (MLWS) and Mean Low Water Neaps (MLWN). Accurate topographic estimation down to MLWS is critical for coastal management, navigation safety, and environmental monitoring. Leveraging a comprehensive dataset from the Wales Coastal Monitoring Centre (WCMC), consisting of over 2000 surveys across 36 coastal survey units, TopoFormer addresses key challenges in topographic prediction, including temporal variability and data gaps in survey measurements. The architecture uniquely combines multi-head attention mechanisms and ConvLSTM layers to capture both long-range dependencies and localized temporal patterns inherent in beach profiles data. TopoFormer's predictive performance was rigorously evaluated against state-of-the-art models, including DenseNet, 1D/2D CNNs, and LSTMs. While all models demonstrated strong performance, \textit{TopoFormer} achieved the lowest mean absolute error (MAE), as low as 2 cm, and provided superior accuracy in both in-distribution (ID) and out-of-distribution (OOD) evaluations.
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