Yuqi Yin

Abstract Sea surface height around Taiwan Island shows complex multi-scale variability, posing a major forecasting challenge. To address this, the Taiwan Island Adjacent Seas Sea Level AI Forecaster (TAS-SLAF), a deep learning framework integrating convolutional layers, ConvLSTM, and the Convolutional Block Attention Module (CBAM) to capture spatiotemporal features, is applied for 15-day sea level anomaly (SLA) prediction in this sea area.

1 Introduction Phylogeographic and molecular dating approaches have become indispensable tools in studies of viral pathogens and molecular epidemiology, enabling researchers to trace the spread of diseases across clinical, environmental, and agricultural contexts (Faria et al. 2011).