IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i3p608-d1046868.html
   My bibliography  Save this article

Automated Fire Extinguishing System Using a Deep Learning Based Framework

Author

Listed:
  • Senthil Kumar Jagatheesaperumal

    (Department of Electronics & Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamil Nadu, India)

  • Khan Muhammad

    (Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea)

  • Abdul Khader Jilani Saudagar

    (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Joel J. P. C. Rodrigues

    (College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266555, China
    Instituto de Telecomunicações, 6201-001 Covilhã, Portugal)

Abstract

Fire accidents occur in every part of the world and cause a large number of casualties because of the risks involved in manually extinguishing the fire. In most cases, humans cannot detect and extinguish fire manually. Fire extinguishing robots with sophisticated functionalities are being rapidly developed nowadays, and most of these systems use fire sensors and detectors. However, they lack mechanisms for the early detection of fire, in case of casualties. To detect and prevent such fire accidents in its early stages, a deep learning-based automatic fire extinguishing mechanism was introduced in this work. Fire detection and human presence in fire locations were carried out using convolution neural networks (CNNs), configured to operate on the chosen fire dataset. For fire detection, a custom learning network was formed by tweaking the layer parameters of CNN for detecting fires with better accuracy. For human detection, Alex-net architecture was employed to detect the presence of humans in the fire accident zone. We experimented and analyzed the proposed model using various optimizers, activation functions, and learning rates, based on the accuracy and loss metrics generated for the chosen fire dataset. The best combination of neural network parameters was evaluated from the model configured with an Adam optimizer and softmax activation, driven with a learning rate of 0.001, providing better accuracy for the learning model. Finally, the experiments were tested using a mobile robotic system by configuring them in automatic and wireless control modes. In automatic mode, the robot was made to patrol around and monitor for fire casualties and fire accidents. It automatically extinguished the fire using the learned features triggered through the developed model.

Suggested Citation

  • Senthil Kumar Jagatheesaperumal & Khan Muhammad & Abdul Khader Jilani Saudagar & Joel J. P. C. Rodrigues, 2023. "Automated Fire Extinguishing System Using a Deep Learning Based Framework," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:608-:d:1046868
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/3/608/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/3/608/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaofei Lin & Shouxin Song & Huaiyuan Zhai & Pengwei Yuan & Mingli Chen, 2020. "Using catastrophe theory to analyze subway fire accidents," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 223-235, February.
    2. Aleksandr Smolin & Andrei Yamaev & Anastasia Ingacheva & Tatyana Shevtsova & Dmitriy Polevoy & Marina Chukalina & Dmitry Nikolaev & Vladimir Arlazarov, 2022. "Reprojection-Based Numerical Measure of Robustness for CT Reconstruction Neural Network Algorithms," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    3. Malka N. Halgamuge & Eshan Daminda & Ampalavanapillai Nirmalathas, 2020. "Best optimizer selection for predicting bushfire occurrences using deep learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 845-860, August.
    4. Jorge Pereira & Jérôme Mendes & Jorge S. S. Júnior & Carlos Viegas & João Ruivo Paulo, 2022. "A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Esmaeil Mohammadian Bishe & Hossein Afshin & Bijan Farhanieh, 2023. "Modified Quasi-Physical Grassland Fire Spread Model: Sensitivity Analysis," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    3. Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    5. Bárbara de Matos & Rodrigo Salles & Jérôme Mendes & Joana R. Gouveia & António J. Baptista & Pedro Moura, 2022. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs," Mathematics, MDPI, vol. 11(1), pages 1-22, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:608-:d:1046868. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.