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False Fire Alarm Detection Using Data Mining Techniques

Author

Listed:
  • Raheel Zafar

    (University of Lahore, Pakistan)

  • Shah Zaib

    (University of Lahore, Pakistan)

  • Muhammad Asif

    (University of Lahore, Pakistan)

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.

Suggested Citation

  • Raheel Zafar & Shah Zaib & Muhammad Asif, 2020. "False Fire Alarm Detection Using Data Mining Techniques," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 12(4), pages 21-35, October.
    • Handle: RePEc:igg:jdsst0:v:12:y:2020:i:4:p:21-35
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    Cited by:

    1. Churchill, Brandyn F., 2021. "How important is the structure of school vaccine requirement opt-out provisions? Evidence from Washington, DC's HPV vaccine requirement," Journal of Health Economics, Elsevier, vol. 78(C).

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