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The paper introduces a novel rainfall-runoff model called RR-Former, which utilizes a Transformer architecture based on attention mechanisms, offering improved flexibility over traditional LSTM-based models. The RR-Former demonstrates superior performance in both individual and regional rainfall-runoff modeling tasks using the CAMELS dataset, achieving higher Nash–Sutcliffe efficiency scores compared to benchmark models. This research highlights the potential of Transformer-based approaches in hydrological

Author
camelliayoloo
Language
EN

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