  This study explores the efficacy of a Transformer model for 120-hour
streamflow prediction across 125 diverse locations in Iowa, US. Utilizing data
from the preceding 72 hours, including precipitation, evapotranspiration, and
discharge values, we developed a generalized model to predict future
streamflow. Our approach contrasts with traditional methods that typically rely
on location-specific models. We benchmarked the Transformer model's performance
against three deep learning models (LSTM, GRU, and Seq2Seq) and the Persistence
approach, employing Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency
(KGE), Pearson's r, and Normalized Root Mean Square Error (NRMSE) as metrics.
The study reveals the Transformer model's superior performance, maintaining
higher median NSE and KGE scores and exhibiting the lowest NRMSE values. This
indicates its capability to accurately simulate and predict streamflow,
adapting effectively to varying hydrological conditions and geographical
variances. Our findings underscore the Transformer model's potential as an
advanced tool in hydrological modeling, offering significant improvements over
traditional and contemporary approaches.
