The rapid advancement of machine learning techniques has led to their widespread application in various domains including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN), for snowmelt-driven discharge modeling in the Himalayan basin of the Hindu Kush Himalayan Region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models including Support Vector Regression (SVR), Long Short Term Memory (LSTM), and Transformer. Furthermore, Nested cross-validation (CV) is used with five outer folds and three inner folds, and hyper-parameter tuning is performed on the inner folds. To evaluate the performance of the model mean absolute error (MAE), root mean square error (RMSE), R square ($R^{2}$), Kling-Gupta Efficiency (KGE), and Nash-Sutcliffe Efficiency (NSE) are computed for each outer fold. The average metrics revealed that TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, $R^{2}$ of 0.991, KGE of 0.992, and NSE of 0.991. The findings of this study demonstrate the effectiveness of the deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of TCN highlights its potential as a promising deep learning model for similar hydrological applications.

翻译：机器学习技术的快速发展使其在水资源等领域得到了广泛应用，然而融雪建模仍是一个尚未被深入探索的领域。本研究提出了一种基于时序卷积网络（TCN）的先进深度学习序列模型，用于兴都库什-喜马拉雅地区喜马拉雅流域的融雪径流模拟。为评估该模型的性能，我们将其与支持向量回归（SVR）、长短期记忆网络（LSTM）和Transformer等其他主流模型进行了对比分析。进一步地，采用五折外循环与三折内循环的嵌套交叉验证（CV）方法，并在内循环中进行超参数调优。为评估模型性能，每个外循环均计算了平均绝对误差（MAE）、均方根误差（RMSE）、决定系数（$R^{2}$）、克林-古普塔效率系数（KGE）和纳什-萨特克利夫效率系数（NSE）。平均指标显示，TCN模型表现优于其他模型，其平均MAE为0.011、RMSE为0.023、$R^{2}$为0.991、KGE为0.992、NSE为0.991。研究结果表明，与传统的机器学习方法相比，深度学习模型在融雪径流预测中更具有效性。此外，TCN的优越性能凸显了其作为类似水文应用中极具潜力的深度学习模型的价值。