Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques

arXiv:2404.13327v1 Announce Type: new
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 …

abstract advancement analysis application art arxiv comparative analysis cs.ai cs.lg deep learning domains forecasting however machine machine learning machine learning techniques modeling resources sota state study type water