Abstract
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.
Abstract (translated)
机器学习技术的快速发展导致其在各种领域得到了广泛应用，包括水资源的预测。然而，雪融建模仍然是一个尚未深入研究的问题。在这项研究中，我们提出了一个最先进的（SOTA）深度学习序列模型，利用 Temporal Convolutional Network (TCN)，对喜马拉雅山脉地区喜马拉雅山脉的雪融驱动流量建模进行研究。为了评估我们提出的模型的性能，我们与其他流行的模型（包括支持向量回归（SVR）、长短时记忆（LSTM）和Transformer）进行了比较分析。此外，我们还使用了嵌套交叉验证（CV），包括五个外层fold和三个内层fold，并在内层fold上进行超参数调整。为了评估模型的性能，我们计算了每个外层fold的均绝对误差（MAE）、均方根误差（RMSE）、相关系数（R平方）和高斯-库格曼效率（KGE）和纳什-苏堤效率（NSE）。平均指标显示，TCN在其他模型中表现优异，平均MAE为0.011，RMSE为0.023，$R^{2}$为0.991，KGE为0.992，NSE为0.991。本研究的结果表明，与传统机器学习方法相比，深度学习模型在雪融驱动流预测方面具有有效性。此外，TCN的卓越性能表明，它有望成为一个有前景的深度学习模型，用于类似的 hydrological 应用。
URL
https://arxiv.org/abs/2404.13327
