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