ConvLSTM Not Always Superior for One-Day Rainfall Prediction
Summary
A study benchmarked ConvLSTM against simpler models for one-day-ahead rainfall prediction across four Indian cities using daily reanalysis grids. The findings indicate that ConvLSTM did not consistently outperform simpler alternatives like FC-LSTM or even persistence, especially for domain-mean rainfall and high-rainfall days.
Why it matters
Data scientists and climate modelers should critically evaluate complex deep learning architectures like ConvLSTM for specific forecasting tasks, as simpler models may offer comparable or better performance with less computational overhead.
How to implement this in your domain
- 1Benchmark multiple model architectures, including simpler ones, before committing to complex deep learning models for time-series forecasting.
- 2Prioritize domain-specific performance metrics (e.g., high-rainfall day detection) over general accuracy when evaluating forecasting models.
- 3Consider the spatial and temporal continuity of data when selecting between convolutional and fully-connected recurrent neural networks.
- 4Investigate the sensitivity of chosen models to recent input lags to understand their predictive mechanisms.
Who benefits
Key takeaways
- ConvLSTM does not consistently outperform simpler models for one-day-ahead rainfall prediction on daily reanalysis grids.
- FC-LSTM and even persistence models can achieve comparable or better performance in certain rainfall prediction scenarios.
- Model selection should be based on strong benchmarking across average, spatial, and high-rainfall performance.
- Gridded inputs alone do not automatically justify the use of ConvLSTM.
Original post by Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj
"arXiv:2607.26581v1 Announce Type: new Abstract: Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether convolutiona…"
View on XOriginally posted by Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj on X · view source
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