ABSTRACT: Lightning, a natural phenomenon, poses substantial risks to life and property, necessitating accurate detection and timely alerts. Traditional methods relying on ground-based sensors have limitations in coverage and accuracy. However, recent advancements in deep learning have revolutionized lightning detection and alert systems. This paper introduces the Lightning Prediction and Alert System (LPAS), employing deep learning to enhance response to lightning threats. LPAS utilizes deep learning, particularly convolutional neural networks (CNNs) to process diverse data sources effectively. These models excel in detecting complex spatiotemporal patterns associated with lightning strikes. Furthermore, LPAS enables real-time lightning detection and alerting, delivering instant notifications through mobile apps, SMS, and email. In summary, the Lightning Prediction and Alert System powered by deep learning signifies a significant leap in lightning prediction technology. Its integration of multimodal data, deep learning models, and real-time alerting capabilities enhances public safety and benefits various industries. By mitigating lightning risks and enhancing our understanding of storm dynamics, LPAS promises a safer future for communities worldwide.
KEYWORDS: Convolutional Neural network, Deep learning, Remote monitoring, Alert system.
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DOI:
10.17148/IJARCCE.2024.13385
