Dynamic Lag Encoding and Multi-Head Attention Improve Deep Learning Flood Forecasting
A deep learning model combining dynamic lag encoding and temporal multi-head attention significantly reduces peak flow forecasting errors across multi-hour ...
A deep learning model combining dynamic lag encoding and temporal multi-head attention significantly reduces peak flow forecasting errors across multi-hour ...
Hydrological modeling of Northwest China's Bortala River reveals that climate-driven runoff growth will reverse around the 2070s as groundwater storage ...
An ensemble of 100 LSTM networks trained on physically based groundwater simulations shows that ensemble spread strongly tracks RMSE and ...
An improved LSTM achieved mean NSE of 0.9633 and mean peak-discharge error of 3.90% in nine held-out flood events from ...
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© 2026 Scienmag - Science Magazine