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Development of an Intelligent Fire Hazard Detection System Using Enhanced Machine Learning Technique

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  • Odo F.E

    (Enugu State University of Science and Technology)

  • Ituma C.

    (Enugu State University of Science and Technology)

  • Asogwa T.C.

    (Enugu State University of Science and Technology)

  • Ebere U.C.

    (Destinet Smart Technologies)

Abstract

This work was targeted on the development of an intelligent fire hazard detection system using enhanced machine learning technique. The study reviewed many literatures which revealed the problems fire hazard has causes over the years, and also the efforts proposed to solve these problems, but despite the success achieved, there is still great room for improvements This was achieved using Dynamic Systems Development Model (DSDM) methodology which accommodates all necessary functionalities such as modeling diagram, mathematical models, algorithms and simulation based implementation. The model of the wavelet transform was developed and the decomposed output was feed to a Feed Forward Neural Network (FFNN) which was trained with fire data collected from the Nigerian Fire Service Department and back propagation algorithm, to achieve an intelligent fire hazard detection algorithm. The algorithm was implemented with Mathlab and then tested. The result showed a regression performance value of 0.96152, accuracy of 93.33% and MSE value of 0.000103Mu which all indicated system reliability

Suggested Citation

  • Odo F.E & Ituma C. & Asogwa T.C. & Ebere U.C., 2022. "Development of an Intelligent Fire Hazard Detection System Using Enhanced Machine Learning Technique," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 7(3), pages 56-62, March.
    • Handle: RePEc:bjf:journl:v:7:y:2022:i:3:p:56-62
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