Disaster Track: AI-Driven Real-Time Monitoring and Instant Alerts for Emergency Response

“Disaster Track” is an AI-driven system designed for real-time monitoring and instant alerts in emergency situations. The system is built around an Arduino Uno microcontroller, which continuously collects environmental data from multiple sensors, including the DHT11 for temperature and humidity, the BMP180 for barometric pressure and temperature, and a gyroscopic sensor for seismic activity detection. This data, combined with manually inputted information, is processed by an AI model to generate highly accurate weather forecasts and seismic alerts. Notifications are then transmitted to users via a GSM SMS module, ensuring timely emergency warnings. Additionally, Python-based algorithms enhance weather prediction, while an integrated IoT platform enables remote data storage and access, facilitating further analysis and improved disaster response planning.

Authors

Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/icvadv63329.2025.10961227
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.48
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Disaster Track: AI-Driven Real-Time Monitoring and Instant Alerts for Emergency Response

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Disaster Track: AI-Driven Real-Time Monitoring and Instant Alerts for Emergency Response

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article en
1 citations

Abstract

“Disaster Track” is an AI-driven system designed for real-time monitoring and instant alerts in emergency situations. The system is built around an Arduino Uno microcontroller, which continuously collects environmental data from multiple sensors, including the DHT11 for temperature and humidity, the BMP180 for barometric pressure and temperature, and a gyroscopic sensor for seismic activity detection. This data, combined with manually inputted information, is processed by an AI model to generate highly accurate weather forecasts and seismic alerts. Notifications are then transmitted to users via a GSM SMS module, ensuring timely emergency warnings. Additionally, Python-based algorithms enhance weather prediction, while an integrated IoT platform enables remote data storage and access, facilitating further analysis and improved disaster response planning.

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Openalex Percentile: Top 11%
Anomaly Detection Techniques and Applications
2.48
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