CFP last date
20 June 2024
Reseach Article

Urban Market Fire Disasters Management in Nigeria: A Damage Minimization based Fuzzy Logic Model Approach

by V.o.oladokun, C.g Emmanuel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 17
Year of Publication: 2014
Authors: V.o.oladokun, C.g Emmanuel
10.5120/18609-9525

V.o.oladokun, C.g Emmanuel . Urban Market Fire Disasters Management in Nigeria: A Damage Minimization based Fuzzy Logic Model Approach. International Journal of Computer Applications. 106, 17 ( November 2014), 1-6. DOI=10.5120/18609-9525

@article{ 10.5120/18609-9525,
author = { V.o.oladokun, C.g Emmanuel },
title = { Urban Market Fire Disasters Management in Nigeria: A Damage Minimization based Fuzzy Logic Model Approach },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 17 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number17/18609-9525/ },
doi = { 10.5120/18609-9525 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:37.503610+05:30
%A V.o.oladokun
%A C.g Emmanuel
%T Urban Market Fire Disasters Management in Nigeria: A Damage Minimization based Fuzzy Logic Model Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 17
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent fire disasters in crowded urban business and market structures have become a major concern to Nigerian urban planners. Urban renewal schemes are being initiated to minimize the occurrence of fire accidents and other associated problems by various levels of government in Nigeria. However there is need for a systematic approach and tools predict the performance of potential intervention schemes and policies and also to ensure that the resources deployed for these schemes achieve the highest possible impact. This study was therefore aimed at developing a soft computing based tool for analyzing fire accident occurrence and prevention systems for commercial complexes. The relevant quantities, key accident causative factors, and their relevant interactions in a fire accident occurrence system were identified through literature search and interview with fire experts. Appropriate linguistic variables and their equivalent term sets were developed for these factors. Using the Matlab fuzzy logic toolbox various possible membership functions were numerically tested to identify the most suitable function for each linguistic variable. A fuzzy inference system of the commercial building fire accidents model was then developed. Model validation was carried out based on data obtained for some randomly selected markets within Ibadan city using structured checklists and expect rating format. Fire risk indexing was used to carry out evaluation by ranking the factors. Four key input factors, namely Users' Safety Culture, Incipient fire Likelihood, Building resistant to fire spread and potential damage level, were identified. The Gaussian and Trapezoidal membership functions were the most suitable. The resulting model was a two stage Mamdani type Fuzzy Inference system using sixteen and twenty-five rule knowledge bases respectively. The checklist and rating format was found easy to use. The performance of the model compared well with results from the literature. It is concluded that Fuzzy logic inference system can serve as a decision support system for fire safety management commercial complexes.

References
  1. V. O. Oladokun, A. Kolawole and C. G. Emmanuel, "Risk Analysis Models of Fire Accidents In Nigeria Commercial Complexes: A Fuzzy Logic Approach," in Proceedings of NIIE 2012 Conference, Benin , 2012.
  2. V. O. Oladokun and F. A. Ishola, "A Risk Analysis Model for Fire Disasters in Commercial Complexes in Nigeria," Pacific Journal of Science and Technology. , vol. 11, no. 2, pp. 376-386, 2010.
  3. E. Volna, Introduction to Soft Computing, 1st ed. , www. bookboo. com, 2013.
  4. S. K. a. K. Ali, "A Fuzzy Logic Approach for Reducing Handover Latency in Wireless Networks," Network Protocols and Algorithms Vol. 2, No. 4 ISSN 1943-3581, pp. 61-87, 2010.
  5. Awasthi, S. S. Chauhan and S. K. Goyal, "A fuzzy multicriteria approach for evaluating environmental performance of suppliers. ," International Journal of Production Economics, vol. 126, pp. 370-378, 2010.
  6. M. Chen, "A fuzzy-based decision-support model for rebuy procurement,. 2009. ," International Journal of Production Economics, vol. 12, no. 2, pp. 714-724, 2009.
  7. E. H. Mamdani, "Applcation of Fuzzy Algorithms for Control of Simple Dynamic Plant," in Proc, IEE 121, 1974.
  8. A. K. Mandal, Introduction to Control Engineering : Modeling Analysis and Design, 1st ed. , New Delhi: New Age International, Publishers , 2006.
  9. V. O. Oladokun and O. O. Okesiji, "Application of Fuzzy Logic to the Optimal Location of Public Utilities: A Case Study of Pedestrian Bridges," in International Proceedings of Economics Development and Research , Dubai, 2012.
  10. L. A. Zadeh, "Soft Compuiting and Fuzzy Logic," IEEE Software, vol. 11, no. 6, pp. 48-56, 1994.
  11. X. Li, D. Ruan and A. J. Van der Wal, "Discussion on Soft Computing at FLIN's 96," International Journal of Inteligent systems, vol. 13, no. 2-3, pp. 287-300, 1998.
  12. L. C. C. Lin, "Individualizing Learning Using Fuzzy Expert System and Variable Learning Route Model," in 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece. , 2008.
  13. J. S. Jang and C. T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hal, 1997.
  14. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1-13, 1975.
  15. L. Zadeh, "Fuzzy sets as a basis for a theory of possibility," Fuzzy Sets and Systems, p. 3–28, 1978.
Index Terms

Computer Science
Information Sciences

Keywords

Fire Accident Fuzzy Logic Fuzzy inference Risk Analysis Safety.