AI-Powered Weather System with Disaster Prediction

Cedric Ngendahimana*
Department of Computer Science, DMI-St. John the Baptist University, Malawi.
Periodicity:October - December'2024

Abstract

This AI-powered weather system enhances disaster prediction and preparedness using advanced machine learning algorithms and real-time meteorological data. Leveraging the OpenWeatherMap API, the system analyzes environmental indicators such as rainfall, temperature, and wind speed to assess risks of floods, droughts, cyclones, hailstorms, and wildfires. Machine learning models, including linear regression and threshold-based heuristics, ensure high predictive accuracy. The threshold-based approach uses historical disaster data to define limits, with exceedances signaling high risk. Linear regression forecasts variables like rainfall and temperature, integrating probabilistic outputs into the heuristic system for improved accuracy. This system demonstrates AI's potential in early warning systems, bolstering community preparedness and enabling timely interventions by authorities.

Keywords

AI (Artificial Intelligence), Weather Prediction, Natural Disaster Alerts, ML (Machine Learning), Disaster Preparedness, GIS (Geographic Information System), API (Application Programming Interface).

How to Cite this Article?

Ngendahimana, C. (2024). AI-Powered Weather System with Disaster Prediction. i-manager’s Journal on Software Engineering, 19(2), 24-30.

References

[5]. Bhattacharyya, S., Mondal, N. K., Mondal, K., Singh, J. P., & Prakash, K. B. (Eds.). (2021). Cognitive Data Models for Sustainable Environment. Academic Press.
[8]. Satishkumar, D., & Sivaraja, M. (Eds.). (2024). Utilizing AI and Machine Learning for Natural Disaster Management. IGI Global.
[9]. Damola, P., & Miracle, A. (2024). Predictive Modeling for Disaster Management. Researchgate.
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