Abstract
Rainfall prediction is a pivotal aspect of climate forecasting, influencing agriculture, water resource management, and disaster preparedness. This comprehensive review explores the integration of advanced algorithms and edge analytics within a fog computing framework to elevate the accuracy of rainfall predictions. The introduction outlines the significance of accurate rainfall predictions, the limitations of traditional methods, and the motivation for embracing fog computing, advanced algorithms, and edge analytics. A detailed examination of fog computing architecture underscores its decentralized nature and proximity to data sources, addressing challenges inherent in centralized models. The integration of edge analytics is discussed in depth, emphasizing its crucial role in preprocessing IMD data at the source. Insights gained from these implementations offer valuable perspectives on the practical implications, successes, and challenges associated with these methodologies.
Top2. Background
Fog Computing facilitates the seamless integration of diverse data sources, including satellite imagery, weather stations, and sensor networks, in real-time. The section explores how the fusion of these heterogeneous data sets at the edge enhances the accuracy and robustness of rainfall prediction models as shown in figure 1.
Discuss the autonomy and self-sufficiency of edge devices empowered by Fog Computing, enabling them to make localized decisions based on analyzed data.
Emphasize the reduced dependency on central servers, making the system more resilient in the face of network disruptions.
Explore the potential of adaptive learning algorithms that continuously evolve based on incoming data, allowing the rainfall prediction system to adapt to changing climate patterns over time.
Examine the function of Fog Computing in facilitating the dynamic retraining of models at the edge to enhance accuracy.
Highlight how Fog Computing's distributed architecture inherently supports scalability, allowing the system to handle an increasing volume of data and computational demands.