AI 中文总结
本研究采用1DCNN、LSTM等深度学习模型填补热带气旋最佳路径数据中缺失的Rmax值，发现时间模型结合R34输入可提升性能，为海岸灾害评估提供了不完整TC记录的重建方法。
AI 中文摘要
概率海岸灾害评估需要准确刻画热带气旋（TC）参数，但数据集常包含关键变量最大风速半径（Rmax）的缺失记录，而Rmax是联合概率法分析中的重要变量。本研究评估用于Rmax填补的数据驱动方法，包括一维卷积神经网络（1DCNNs）、长短期记忆（LSTM）网络及传统机器学习模型。我们采用物理信息输入增强、时间建模和迁移学习，使用合成RAFT与STORM数据集进行预训练，观测性IBTrACS数据用于微调。纳入34节风半径（R34）可显著提升所有模型类型的性能；时间模型虽使用样本量约少一个数量级，但平均相关性高于非时间模型，表明其能更好保留不同气旋间Rmax的相对变异性，且当R34不可用时该优势更显著，说明时间信息可部分补偿缺失的气旋规模预测因子。迁移学习未提升性能，可能因合成数据集的Rmax分布低于IBTrACS且变异性更小。这些发现证明了时间深度学习在重建不完整气旋记录中的潜力，并强调物理信息输入、观测数据可用性及分布一致性在海岸灾害评估中的重要性。
英文摘要
Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax imputation, including one-dimensional Convolutional Neural Networks (1DCNNs), Long Short-Term Memory (LSTM) networks, and conventional machine learning models. We examine physics-informed input augmentation, temporal modeling, and transfer learning using synthetic RAFT and STORM datasets for pre-training and observational IBTrACS data for fine-tuning. Including the radius of 34-knot winds (R34) substantially improves performance across all model types. Temporal models achieve higher average correlations than non-temporal models despite using approximately an order of magnitude fewer samples, indicating better preservation of relative Rmax variability across storms. This advantage is more pronounced when R34 is unavailable, suggesting temporal information can partially compensate for missing storm-size predictors. Transfer learning does not improve performance, likely because synthetic datasets have lower and less variable Rmax distributions than IBTrACS. These findings demonstrate the potential of temporal deep learning for reconstructing incomplete TC records and highlight the importance of physics-informed inputs, observational data availability, and distributional consistency in coastal hazard assessment.
