  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.
