AI/ML Integration on Edge Computing for More Accurate Weather Predictions

Authors

  • Muhammad Faiz Raihan Ihwan College of Meteorology Climatology and Geophysics Author

DOI:

https://doi.org/10.53842/sphgkp71

Keywords:

Artificial Intelligence , Artificial Intelligence, Machine Learning, Machine Learning, Edge Computing, Weather Prediction, Weather Prediction, Edge Computing, Real-time Processing, Real-time Processing

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into edge computing systems presents a promising avenue for achieving highly accurate weather predictions. By leveraging real-time data collection, processing, and analysis capabilities directly on edge devices, this paper outlines a practical framework for improving predictive accuracy. We explore the challenges, advantages, and methodologies of deploying ML models on edge devices for weather forecasting applications. This study incorporates recent advancements in edge computing and AI algorithms, supported by a case study that demonstrates real-world implementation and results.

Author Biography

  • Muhammad Faiz Raihan Ihwan, College of Meteorology Climatology and Geophysics

    Instrumentation-MKG Study Program, College of Meteorology Climatology and Geophysics, Tangerang, Banten, Indonesia

References

Loseto, G., Scioscia, F., Ruta, M., Gramegna, F., Ieva, S., Fasciano, C., Bilenchi, I., & Loconte, D. (2022). Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems. Sensors, 22(6), 2166. https://doi.org/10.3390/s22062166

Lukacz, P. M. (2024). Developing AI for Weather Prediction. Science & Technology Studies. https://doi.org/10.23987/sts.125741

Reddy, Y. L. P. (2021). Exploring Edge Computing in Artificial Intelligence Using Service Aggregation Standards. International Journal of Communication and Information Technology, 2(1), 1–4. https://doi.org/10.33545/2707661x.2021.v2.i1a.20

Singh, K. D. (2023). Fog-Based Edge AI for Robotics: Cutting-Edge Research and Future Directions. Eai Endorsed Transactions on Ai and Robotics, 2. https://doi.org/10.4108/airo.3619

Xu, H. (2024). Improvement of Disastrous Extreme Precipitation Forecasting in North China by Pangu-Weather AI-driven Regional WRF Model. Environmental Research Letters, 19(5), 54051. https://doi.org/10.1088/1748-9326/ad41f0

Downloads

Published

13-03-2022

Data Availability Statement

None

How to Cite

AI/ML Integration on Edge Computing for More Accurate Weather Predictions. (2022). Journal of Computation Physics and Earth Science (JoCPES), 2(1). https://doi.org/10.53842/sphgkp71

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