One to Three‐Day Lead Streamflow Forecast Using Multi‐Head Attention With Long Short‐Term Memory in Reservoir Regulated Catchment

Abstract

Accurate, timely, and actionable flood forecasting has potential for reducing flood risk but remains challenging due to uncertainties in meteorological forecasts, poor hydrological observations, and increasing forecast errors with lead time. While Long Short‐Term Memory (LSTM) models have surpassed conventional hydrological forecasting models, they often struggle to capture the timing and magnitude of extreme floods due to long‐range dependencies and static feature importance. Here, we propose a sequential LSTM with Multi‐Head Attention (SeqLSTM‐MHA) to improve 1–3 day ahead flood forecasts, explicitly using historical hydrometeorological and upstream hydrological observations. SeqLSTM‐MHA attained an NSE of 0.85–0.81 for water level and 0.74–0.71 for streamflow corresponding to t +1 to t +3 day lead, outperforming benchmark models (traditional LSTMs, bidirectional LSTM, CatBoost, and multiple linear regression). The ablation analysis shows that notable improvements at longer lead times ( t +2 and t +3) arise from the interaction of sequential forecasting, hierarchical upstream representation, and attention‐based temporal weighting rather than merely increased model capacity. Our model demonstrates strong skill in forecasting extremes (NSE >0.6) and correctly captures more than 80% of peak streamflow during the extreme events exceeding the 95th percentile threshold. Time‐resolved attention analysis shows that recent rainfall primarily drives immediate responses, while upstream flow propagation becomes more important at longer lead times. Overall, our findings demonstrate the potential of attention‐based LSTM for operational flood forecasting, particularly for high‐impact events.