Document Type
Article
Source of Publication
Mathematical Modelling of Engineering Problems
Publication Date
1-1-2025
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 0.0802×10⁻3 and RMSE = 0.009, while in Karak, the MSE was 0.118×10⁻3 and RMSE = 0.0109. In Amman, the RNN using all features achieved MSE = 0.0167×10⁻3 and RMSE = 0.0041.
DOI Link
ISSN
Publisher
International Information and Engineering Technology Association
Volume
12
Issue
7
First Page
2456
Last Page
2466
Disciplines
Computer Sciences
Keywords
Convolutional Neural Network-Recurrent Neural Network (CNN-RNN), deep learning, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), weather forecast
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Al-Samrraie, La'aly A.; Abdalla, Ayman M.; Alrawashdeh, Khalideh Al Bkoor; Al Bsoul, Abeer; Awad, Mohammad Abu; Alzboon, Kamel; and Al-Taani, Ahmed A., "Deep Learning Models Based on CNN, RNN, and LSTM for Rainfall Forecasting: Jordan as a Case Study" (2025). All Works. 7535.
https://zuscholars.zu.ac.ae/works/7535
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series