A Comparative Analysis of Deep Learning Approaches for Rainfall Forecasting in Taiwan
Bibliographic record
Abstract
Forecasting rainfall is critical for agriculture, urban planning, and disaster management.This study evaluated the performance of three models: LSTM, CNN+LSTM, and BiLSTM, for forecasting rainfall in Hualien City, situated on Taiwan's eastern coast.The dataset utilized was sourced from the Department of Atmospheric Sciences at Chinese Culture University, covering the period from 1998 to 2018.This comprehensive dataset includes measurements such as datetime, temperature, humidity, air pressure, wind direction, and wind speed, providing a robust foundation for predictive modelling.The study's findings demonstrated that the BiLSTM model significantly outperformed the other models, with an MSE of 6.21, an MAE of 0.56, and an RMSE of 2.49.These findings underscore the BiLSTM's superior ability to identify temporal dependencies and handle the complexities of atmospheric data compared to the simpler LSTM and hybrid CNN+LSTM models.This study improves our understanding of deep learning applications in meteorological forecasting and demonstrates the efficacy of the BiLSTM model in managing the intricacies of time-series data processing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".