Summary of Ensemble Quantile-based Deep Learning Framework For Streamflow and Flood Prediction in Australian Catchments, by Rohitash Chandra et al.
Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
by Rohitash Chandra, Arpit Kapoor, Siddharth Khedkar, Jim Ng, R. Willem Vervoort
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph); Applications (stat.AP); Machine Learning (stat.ML)
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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 deep learning framework for predicting large-scale streamflow forecasts, focusing on Australia’s climate extremes like floods. The ensemble quantile-based model addresses issues with missing data and model calibration. The authors evaluate various univariate and multivariate models and catchment strategies using the CAMELS dataset. They also implement a multistep time-series prediction model to predict flood probability. The results show notable efficacy in streamflow forecasts, with varying uncertainties depending on catchment properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A deep learning framework is developed to forecast floods in Australia. The framework uses historical data and multiple models to make predictions. It’s tested using real-world data from different regions. The results are promising, showing that the model can accurately predict flood probability. This could be used as an early warning system for flooding. |
Keywords
» Artificial intelligence » Deep learning » Probability » Time series
