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False Fire Alarm Detection Using Data Mining Techniques

False Fire Alarm Detection Using Data Mining Techniques

Raheel Zafar, Shah Zaib, Muhammad Asif
Copyright: © 2020 |Volume: 12 |Issue: 4 |Pages: 15
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781799806004|DOI: 10.4018/IJDSST.2020100102
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MLA

Zafar, Raheel, et al. "False Fire Alarm Detection Using Data Mining Techniques." IJDSST vol.12, no.4 2020: pp.21-35. http://doi.org/10.4018/IJDSST.2020100102

APA

Zafar, R., Zaib, S., & Asif, M. (2020). False Fire Alarm Detection Using Data Mining Techniques. International Journal of Decision Support System Technology (IJDSST), 12(4), 21-35. http://doi.org/10.4018/IJDSST.2020100102

Chicago

Zafar, Raheel, Shah Zaib, and Muhammad Asif. "False Fire Alarm Detection Using Data Mining Techniques," International Journal of Decision Support System Technology (IJDSST) 12, no.4: 21-35. http://doi.org/10.4018/IJDSST.2020100102

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Abstract

In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.

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