An IoT-Driven Federated Learning Method for Rainfall Prediction Employing Attention Convolutional Recurrent Networks and Golden Jackal Optimization Techniques
Received: 6 May 2025 | Revised: 24 June 2025 and 4 July 2025 | Accepted: 8 July 2025 | Online: 1 September 2025
Corresponding author: J. Jagadeesan
Abstract
Forecasting heavy rainfall poses a significant challenge for meteorological departments, as it is closely related to human life and the economy, being also a reason for the natural calamities, such as droughts and floods, that people face around the world each year. The precision of rainfall prediction is of enormous significance for countries such as India, whose economy is mainly dependent on agriculture. Rainfall prediction supports evading floods, saving properties, and human lives. There is a need to improve cooperative platforms for weather forecasting on large-scale meteorological data to address the global climate challenge. With the rapid improvement of Artificial Intelligence (AI), Machine Learning (ML) is progressively becoming popular for forecasting rainfall. Federated Learning (FL) is a subset of ML that has become a popular technology for data analysis for Internet of Things (IoT) applications. The innovative growth of IoT-based methods improves smart agriculture, leading to advanced agricultural systems that progressively increase crop yields, reduce irrigation waste, and make it more profitable. This study presents a Method for Rainfall Prediction by employing Attention Convolutional Recurrent Networks and the Golden Jackal Optimization (EMRF-ACRNGJO) approach. The proposed technique initially performs data preprocessing in various stages, such as missing value handling, duplicate removal, categorical to numerical encoding, and scaling. An Attention-based Convolutional Recurrent Neural Network (A-CRNN) is utilized for classification tasks, using the Golden Jackal Optimization (GJO) method for the hyperparameter tuning process. The EMRF-ACRNGJO method was tested on a benchmark dataset, demonstrating an accuracy of 73.30%.
Keywords:
federated learning, rainfall prediction, Golden Jackal Optimization (GJO), attention convolutional recurrent network, Internet of Things (IoT)Downloads
References
B. Keswani, A. G. Mohapatra, P. Keswani, A. Khanna, D. Gupta, and J. Rodrigues, "Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism," Enterprise Information Systems, vol. 14, no. 9–10, pp. 1494–1515, Nov. 2020.
P. Schweizer, "Neutrosophy for physiological data compression: in particular by neural nets using deep learning," International Journal of Neutrosophic Science, pp. 74–80, 2020.
E. Khosla, R. Dharavath, and R. Priya, "Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression," Environment, Development and Sustainability, vol. 22, no. 6, pp. 5687–5708, Aug. 2020.
P. Shao, J. Feng, P. Zhang, and J. Lu, "Interpretable spatial-temporal attention convolutional network for rainfall forecasting," Computers & Geosciences, vol. 185, Mar. 2024, Art. no. 105535.
S. Jayasree and K. R. Ananthapadmanaban, "Discrete Migratory Bird Optimizer with Deep Learning Driven Cyclone Intensity Prediction on Remote Sensing Images," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21605–21610, Apr. 2025.
I. Salehin, I. M. Talha, Md. Mehedi Hasan, S. T. Dip, Mohd. Saifuzzaman, and N. N. Moon, "An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network," in 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India, Dec. 2020, pp. 5–8.
R. Peeriga et al., "Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15805–15812, Aug. 2024.
D. Kaplun et al., "An intelligent agriculture management system for rainfall prediction and fruit health monitoring," Scientific Reports, vol. 14, no. 1, Jan. 2024, Art. no. 512.
T. Akilan and K. M. Baalamurugan, "Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture," Expert Systems with Applications, vol. 249, Sep. 2024, Art. no. 123468.
S. S. Srikar and P. Yellamma, "An IoT-based Intelligent Irrigation and Weather Forecasting System," Recent Patents on Engineering, vol. 18, no. 9, pp. 100–108, Dec. 2024.
F. M. Talaat, "Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes," Neural Computing and Applications, vol. 35, no. 23, pp. 17281–17292, Aug. 2023.
J. Nithyashri, R. K. Poluru, S. Balakrishnan, M. Ashok Kumar, P. Prabu, and S. Nandhini, "IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model," Measurement: Sensors, vol. 29, Oct. 2023, Art. no. 100877.
S. Indhumathi, S. Aghalya, S. J. A, and P. Aarthi M, "IoT-Enabled Weather Monitoring and Rainfall Prediction using Machine Learning Algorithm," in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, Aug. 2023, pp. 1491–1495.
M. Ehteram, F. Barzegari Banadkooki, and M. Afshari Nia, "Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation," Stochastic Environmental Research and Risk Assessment, vol. 38, no. 5, pp. 1803–1830, May 2024.
W. Zou, J. Ji, Y. Wang, J. Wang, Y. Qian, and J. Liu, "Convolutional LSTM with Self-Attention Mechanism for Extreme Weather Prediction," in 2023 China Automation Congress (CAC), Chongqing, China, Nov. 2023, pp. 6782–6787.
S. F. Tekin, A. Fazla, and S. Serdar Kozat, "Numerical Weather Forecasting Using Convolutional-LSTM With Attention and Context Matcher Mechanisms," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–13, 2024.
A. K. Shaikh, A. Nazir, N. Khalique, A. S. Shah, and N. Adhikari, "A new approach to seasonal energy consumption forecasting using temporal convolutional networks," Results in Engineering, vol. 19, Sep. 2023, Art. no. 101296.
S. Monaco, L. Monaco, D. Apiletti, R. Cremonini, and S. Barbero, "Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation," Computers & Geosciences, vol. 205, Nov. 2025, Art. no. 105992.
W. Zhao, Z. Zhang, N. Khodadadi, and L. Wang, "A deep learning model coupled with metaheuristic optimization for urban rainfall prediction," Journal of Hydrology, vol. 651, Apr. 2025, Art. no. 132596.
C. Sun et al., "A convolutional recurrent neural network with attention framework for speech separation in monaural recordings," Scientific Reports, vol. 11, no. 1, Jan. 2021, Art. no. 1434.
S. Jiang, Y. Yue, C. Chen, Y. Chen, and L. Cao, "A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application," Biomimetics, vol. 9, no. 5, May 2024, Art. no. 270.
"weatherAUS." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/trisha2094/weatheraus.
Downloads
How to Cite
License
Copyright (c) 2025 J. Jagadeesan, R. Nagarajan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
