Summary of Comparative Analysis on Snowmelt-driven Streamflow Forecasting Using Machine Learning Techniques, by Ukesh Thapa et al.
Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
by Ukesh Thapa, Bipun Man Pati, Samit Thapa, Dhiraj Pyakurel, Anup Shrestha
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a state-of-the-art deep learning sequential model using Temporal Convolutional Networks (TCNs) for snowmelt-driven discharge modeling in the Himalayan basin. The authors compare their proposed model with other popular models, including Support Vector Regression (SVR), Long Short Term Memory (LSTM), and Transformer. The results show that TCN outperforms the other models, achieving an average mean absolute error of 0.011, root mean square error of 0.023, R-squared value of 0.991, Kling-Gupta Efficiency of 0.992, and Nash-Sutcliffe Efficiency of 0.991. The study demonstrates the effectiveness of deep learning models for snowmelt-driven streamflow forecasting, highlighting their potential as a promising approach for similar hydrological applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how machine learning can help predict water flows in mountainous regions. Scientists used special AI models to forecast streamflow based on snowmelt patterns. They tested different models and found that one called Temporal Convolutional Networks (TCN) performed best. This model was better than others at predicting streamflow, which is important for managing water resources. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Machine learning » Regression » Transformer
