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This research aims to develop an AI-powered early warning system to enhance cyclone prediction in coastal Kenya.

March 7, 2026Open Access

Development and Testing of AI-Powered Early Warning Systems for Cyclone Prevention in Coastal Kenya

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Demonstrates effective cyclone prediction in coastal Kenya, suggesting improved disaster management capabilities.

This research aims to develop an AI-powered early warning system to enhance cyclone prediction in coastal Kenya.
Utilized machine learning algorithms trained on historical weather data.
Measured predictions' sensitivity (at least 85%) and specificity (above 90%).
Quantified prediction uncertainty with a Bayesian hierarchical model.
Assessed the AI system's accuracy (87.6%) and false alarm rate (2.4%) in real-world conditions.
Achieved a prediction accuracy of 87.6% for cyclones.
Obtained sensitivity of at least 85% and specificity above 90%.
Demonstrated a false alarm rate of just 2.4%.
Provided a 95% credible interval around prediction probabilities.

Kinyanjui et al. (2008) studied this question.

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