Deep Learning Models Based on CNN, RNN, and LSTM for Rainfall Forecasting: Jordan as a Case Study
Bibliographic record
Abstract
This study is the first to compare deep learning models for rainfall prediction across several Jordanian cities representing diverse climates using 11 years of recorded climate data, something that previous studies have not addressed in the Jordanian context.The climate records for four Jordanian cities (Amman, Irbid, Karak, and Ajloun) were recorded hourly.The data was divided into training sets (80%) and test sets (20%), with and without the application of correlation analysis, feature selection, and data standardization steps applied.Three neural network models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) were used to evaluate the performance of rainfall prediction in the four cities using three sets of features: all variables (13 features), precipitation only, and a selection of eight correlated features.The results showed that the RNN model outperformed the others overall, especially when using correlated features, recording the lowest error values, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) in most cities, with the exception of Amman, where the model performed best when using all features.Whereas in Irbid, the MSE was 0.080210 3 and RMSE = 0.009, while in Karak, the MSE was 0.11810 3 and RMSE = 0.0109.In Amman, the RNN using all features achieved MSE = 0.016710 3 and RMSE = 0.0041.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".