Exploring the Potential of Containerized GANs: Bridging Docker to Forecast Future Weather Images from Current Weather Data
Keywords:
Containerization, GANs, Docker, Forecasting, Weather Images, DevOpsAbstract
This research delves into the promising realm of containerized Generative Adversarial Networks (GANs), focusing on the innovative fusion of Docker containers and advanced machine learning techniques for the purpose of forecasting future weather images from current weather data. In an era where accurate weather predictions are indispensable for various sectors such as agriculture, transportation, and disaster management, the development of a cutting-edge forecasting tool holds paramount importance. The study commences with an examination of the fundamental concepts underlying GANs. Proposed GANs architecture, excels in producing high-quality image predictions. It calculates loss between predicted image and real image not only after 15 minutes but also after 15×TimeStep minutes. The novel approach of containerization, employing Docker, is introduced as a means of efficiently encapsulating the GANs model and its dependencies, ensuring seamless deployment across different computing environments. The core of this research lies in the exploration of the synergy between GANs and Docker containers. Through an intricate fusion of image generation and container orchestration, the study demonstrates how this innovative amalgamation can revolutionize weather forecasting. By using current weather data as input, GANs leverages its generative power to create realistic future weather images. Docker containers not only enhance the portability and reproducibility of the model but also provide scalability for real-time data processing. The research results unveil the promising potential of containerized GANs as a revolutionary tool for improving the accuracy of weather forecasts. It offers significant advantages in terms of computational efficiency, adaptability, and ease of deployment. The findings hold promise for a wide range of applications in meteorology, disaster preparedness, and climate science. In conclusion, this study illuminates the innovative approach of harnessing containerization techniques within the context of GANs for weather prediction, offering a glimpse into the future of enhanced forecasting capabilities with the potential to benefit numerous industries reliant on accurate weather predictions.
Downloads
References
D. Reis, B. Piedade, F. F. Correia, J. P. Dias and A. Aguiar, "Developing Docker and Docker-Compose Specifications: Developers’ Survey," in IEEE Access, vol. 10, pp. 2318-2329, 2022, doi: 10.1109/ACCESS.2021.3137671.W.-K. Chen, Linear Networks and Systems. Belmont, CA, USA: Wadsworth, 1993, pp. 123–135.
N. Fathima F. and H. Y. Vani, "Building, Deploying and Validating a Home Location Register (HLR) using Jenkins under the Docker and Container Environment," 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020, pp. 1278-1282, doi: 10.1109/ICOSEC49089.2020.9215458.
Y. Cao and H. Wang, "Virtual Cluster Deployment Model for Large-Scale Data Processing Jobs," in IEEE Access, vol. 8, pp. 131870131884, 2020, doi: 10.1109/ACCESS.2020.3009826.
Y. Li and Y. Xia, "Auto-scaling web applications in hybrid cloud based on docker," 2016 5th International Conference on Computer Science and Network Technology (ICCSNT), Changchun, China, 2016, pp. 75-79, doi: 10.1109/ICCSNT.2016.8070122.
V. Sharma, H. K. Saxena and A. K. Singh, "Docker for Multicontainers Web Application," 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 2020, pp. 589-592, doi: 10.1109/ICIMIA48430.2020.9074925.
K. M. S. A. Hennayake, R. Dinalankara and D. Y. Mudunkotuwa, "Machine Learning Based Weather Prediction Model for Short Term Weather Prediction in Sri Lanka," 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), Negambo, Sri Lanka, 2021, pp. 299-304, doi: 10.1109/ICIAfS52090.2021.9606077.
S. Nigam, M. Gupta, A. Shrinivasan, A. V. S. Uttej, C. Kumari and P. B. Disha, "Comparative Study to determine Accuracy for Weather Prediction using Machine Learning," 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1-4, doi: 10.1109/ICCCI56745.2023.10128556.
T. Wardah, A. A. Kamil, A. B. Sahol Hamid and W. W. I. Maisarah, "Statistical verification of numerical weather prediction models for quantitative precipitation forecast," 2011 IEEE Colloquium on Humanities, Science and Engineering, Penang, Malaysia, 2011, pp. 88-92, doi: 10.1109/CHUSER.2011.6163865.
P. de Rosnay et al., "SMOS Data Assimilation for Numerical Weather Prediction," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 1447-1450, doi: 10.1109/IGARSS.2018.8519369.
A. P. Phyu Aung, X. Wang, R. Yu, B. An, S. Jayavelu and X. Li, "DO-GAN: A Double Oracle Framework for Generative Adversarial Networks," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 11265-11274, doi: 10.1109/CVPR52688.2022.01099.
X. Long, X. Wang, R. A. Calix, T. -H. Kim and K. Jiang, "Mapping Chinese Oracle-bone Glyphs to Simplified Glyphs with Generative Adversarial Networks," 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2021, pp. 0352-0357, doi: 10.1109/UEMCON53757.2021.9666588.
Z. Bin, S. Shuai, G. Zhi-chun and H. Jian-feng, "Design and Implementation of Incremental Data Capturing in Wireless Network Planning based on Log Mining," 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021, pp. 2757-2761, doi: 10.1109/IAEAC50856.2021.9390978.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
