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.

该研究探讨了 Transformer 模型在美国爱荷华州 125 个不同位置的 120 小时溪流预测中的有效性。利用前 72 小时的数据，包括降雨量、蒸发蒸腾量和流量值，我们开发了一个通用模型来预测未来的溪流流量。我们的方法与通常依赖于特定位置模型的传统方法相反。我们将 Transformer 模型的性能与三个深度学习模型（LSTM、GRU 和 Seq2Seq）以及持久性方法进行了基准测试，利用 Nash-Sutcliffe 效率（NSE）、Kling-Gupta 效度（KGE）、Pearson 相关系数和归一化均方根误差（NRMSE）作为度量标准。研究结果显示 Transformer 模型的卓越性能，具有更高的中位数 NSE 和 KGE 分数，并展现出最低的 NRMSE 值。这表明它能够准确模拟和预测溪流流量，并能有效适应不同的水文条件和地理变化。我们的发现强调了 Transformer 模型在水文建模中作为先进工具的潜力，相比传统和现代方法具有显著改进。