Physics-informed graph attention LSTM with a novel spatial season-aware GPD claims to outperform baselines and SEAS5 for extreme rainfall prediction across Thailand gauge stations.
Recurrent Neural Networks (RNNs), LSTM, and Gated Recurrent Unit (GRU) were employed for sequence-to-sequence prediction (Hochreiter and Schmidhuber, 1997; Cho et al., 2014)
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Leveraging Teleconnections with Physics-Informed Graph Attention Networks for Long-Range Extreme Rainfall Forecasting in Thailand
Physics-informed graph attention LSTM with a novel spatial season-aware GPD claims to outperform baselines and SEAS5 for extreme rainfall prediction across Thailand gauge stations.