Early fire hazard risk management model in urban environments : Leveraging optimized deep learning techniques
*Vishal GoyalCorresponding authorvishal.glaitm@gmail.comMuma College of Business University of South Florida 8350 N. Tamiami Trail Sarasota; Department of Electronics & Communication Engineering GLA UniversityMuma College of Business University of South Florida 8350 N. Tamiami Trail SarasotaFlorida, FL 34243, U.S.A.View full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business University of South Florida 8350 N. Tamiami Trail SarasotaFlorida, 33620, USA0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500100, India0000-0002-7616-6088View full profile → , Tulika Chakrabartitulika.chakrabarti@spsu.ac.inDepartment of Chemistry Sir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndiaView full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and Engineering Sir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndia0000-0001-8062-4144View full profile → , B. Sivaneasansivaneasan@singaporetech.edu.sgSingapore Specialist Adult Educator Engineering Electrical Power Engineering Programme Singapore Institute of Technology 1 Punggol Coast RoadDepartment of Computer Science and Engineering KG Reddy College of Engineering and Technology Moinabad, Telangana, 500075, IndiaView full profile → , Martin MargalaMartin.margala@louisiana.eduSchool of Computing and Informatics University of Louisiana at LafayetteLafayette, LA, 70503 (337) 482-6768, 70503 (337) 482-6768View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 14 Feb 2024
- Published Online:
- 15 Jan 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1318
- Pages:
- 121–131
Abstract
Keywords
Subject Classifications
References
[1] X. Hong, W. Wang, and Q. Liu, “Design and realization of fire detection using computer vision technology,” in 2019 Chinese Control and Decision Conference (CCDC), pp. 5645–5649 , June (2019).
[2] F. Zhang, P. Zhao, S. Xu, Y. Wu, X. Yang, and Y. Zhang, “Integrating multiple factors to optimize watchtower deployment for wildfire detection,” Sci. Total Environ., vol. 737, p. 139561 (2020).
[3] P. Barmpoutis, K. Dimitropoulos, K. Kaza, and N. Grammalidis, “Fire detection from images using faster R-CNN and multidimensional texture analysis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8301–8305, May (2019).
[4] Y. Valikhujaev, A. Abdusalomov, and Y. Cho, “Automatic fire and smoke detection method for surveillance systems based on dilated CNNs,” Atmosphere, vol. 11, p. 1241 (2020).
[5] C. Cao, “Study of flame detection based on improved YOLOv4,” J. Phys., vol. 1952 (2021).
[6] B. Kim and J. Lee, “A video-based fire detection using deep learning models,” Appl. Sci., vol. 9, p. 2862 (2019).
[7] S. Padmalal, I. E. Dayanand, G. S. Rao, T. S. Reddy, A. Ravuri, V. C., and S. Gore, “Securing the skies: Cybersecurity strategies for smart city cloud using various algorithms,” IJRITCC (2023).
[8] P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, “A review on early forest fire detection systems using optical remote sensing,” Sensors, vol. 20, no. 22, p. 6442 (2020).
[9] Y. Zhang, P. Geng, C. B. Sivaparthipan, and B. A. Muthu, “Big data and artificial intelligence based early risk warning system of fire hazard for smart cities,” Sustain. Energy Technol. Assess., vol. 45, p. 100986 (2021).
[10] Z. A.-Thalabi, S. Hussein, A. A. Heydari, and M. Tavakoli, “Modeling and prediction using an artificial neural network to study the impact of foreign direct investment on the growth rate: A case study of the State of Qatar,” J. Stat. Manage. Syst., vol. 25, no. 8, pp. 1991–2003 (2022).
[11] Y. Zhang, P. Geng, C. B. Sivaparthipan, and B. A. Muthu, “Big data and artificial intelligence based early risk warning system of fire hazard for smart cities,” Sustain. Energy Technol. Assess., vol. 45, p. 100986 (2021).
[12] S. Gore, I. Dutt, R. P. Dahake, H. E. Khodke, S. L. Kurkute, B. J. Dange, and S. Gore, “Innovations in smart city water supply systems,” Int. J. Intell. Syst. Appl. Eng. (2023).
[13] A. Bhatia, B. Roy, and A. Kumar, “A review of tourism sustainability in the era of Covid-19,” J. Stat. Manage. Syst., vol. 25, no. 8, pp. 1871–1888 (2022).
[14] M. Peixoto, “A survey of emergencies management systems in smart cities,” (2023).
[15] D. G. Costa, J. P. J. Peixoto, T. C. Jesus, P. Portugal, F. Vasques, E. Rangel, and M. Peixoto, “A survey of emergencies management systems in smart cities,” IEEE Access (2022).
[16] H. G. Akdemir and H. G. Kocken, “A new fuzzy linear regression algorithm based on the simulation of fuzzy samples and an application on popularity prediction of Covid-19 related videos,” J. Stat. Manage. Syst., vol. 25, no. 8, pp. 2025–2041 (2022).
[17] R. S. Mendle and A. Hartung, “Wielding a concept with two edges: How to make use of the smart cities concept and understanding its risks from the resilient cities perspective,” in Resilient Smart Cities, Cham: Springer, pp. 375–394 (2022).
[18] S. Gore, S. Hamsa, S. Roychowdhury, G. Patil, S. Gore, and S. Karmode, “Augmented intelligence in machine learning for cybersecurity: Enhancing threat detection and human-machine collaboration,” in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India (2023).
[19] S. Tarar and N. Bhasin, “Fire hazard detection and prediction by machine learning techniques in smart buildings (SBs) using sensors and unmanned aerial vehicles (UAVs),” in Digital Cities Roadmap: IoT-Based Architecture and Sustainable Buildings, pp. 63–95 (2021).
[20] F. Ullah, S. Qayyum, M. J. Thaheem, F. Al-Turjman, and S. M. Sepasgozar, “Risk management in sustainable smart cities governance: A TOE framework,” Technol. Forecast. Soc. Change, vol. 167, p. 120743 (2021).
