Analysis and Forecasting of Fire incidence in Davao City
DOI:
https://doi.org/10.32871/rmrj2008.02.03Keywords:
R programming, forecasting, fire incidence, modeling for decision makingAbstract
Fire incidence is a big problem for every local government unit in the Philippines. The two most detrimental effects of fire incidence are economic loss and loss of life. To mitigate these losses, proper planning and implementation of control measures must be done. An essential aspect of planning and control measures is prediction of possible fire incidences. This study is conducted to analyze the historical data to create a forecasting model for the fire incidence in Davao City. Results of the analyses show that fire incidence has no trend or seasonality, and occurrences of fire are neither consistently increasing nor decreasing over time. Furthermore, the absence of seasonality in the data indicate that surge of fire incidence may occur at any time of the year. Therefore, fire prevention activities should be done all year round and not just during fire prevention month.
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Ahn, S., Kang, H., Cho, J., Kim, T. O., & Shin, D. (2015). Forecasting model design of fire occurrences with ARIMA models. Journal of the Korean Institute of Gas, 19(2), 20-28. https://doi.org/10.7842/KIGAS.2015.19.2.20
Barker, J. (2005) Abstraction and Modeling. In: Beginning Java Objects. Apress
Benjamin, P., Erraguntla, M., Delen, D., & Mayer, R. (1998). Simulation modeling at multiple levels of abstraction. 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274), 1, 391-398. 10.1109/WSC.1998.745013
Burroughs, M. (2016, September). International fire statistics. Retrieved from https://www.frsug.org/reports/International_Fire_Statistics_September_2016.pdf
Colina, A. (2015, December 29). Fire incidents increase in Davao region. https://www.mindanews.com/top-stories/2015/12/66020/
Commission on Audit (COA). (2018, December). Bureau of fire protection modernization program. Retrieved from https://www.coa.gov.ph/index.php/bureau-of-fire-protection-modernization-program#
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE transactions on power systems, 18(3). 10.1109/TPWRS.2002.804943
Hyndman, R. J. (2001). Box-Jenkins modeling. In Regional Symposium on Environment and Natural Resources, Apr (pp. 10-11). Retrieved from https://robjhyndman.com/papers/BoxJenkins.pdf
Kavasseri, R. G., & Seetharaman, K. (2009). Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy, 34(5), 1388-1393. 10.1016/j.renene.2008.09.006
Keshvani, A. (2013, August 7). How to use the autocorrelation function (ACF)? Retrieved from Coolstatblog: https://coolstatsblog.com/2013/08/07/howto-use-the-autocorreation-function-acf/
Kurbalija V., Radovanović M., Geler Z., Ivanović M. (2010) A Framework for Time-Series Analysis. In: Dicheva D., Dochev D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science, vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_5
Manayaga, M. C. E., & Ceballos, R. F. (2019). Forecasting the remittances of the Overseas Filipino Workers in the Philippines. International Journal of Statistics and Economics ,20(3),36-44. http://www.ceser.in/ceserp/index.php/bse/article/view/6126
Montgomery, D. C., Jennings, C. L., & Kulachi, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
National Disaster Risk Reduction and Management Council (NDRRMC). (2017). Fire incidence in Bankerohan Public Market. Retrieved from http://ndrrmc.gov.ph/20-incidents-monitored/3252-fire-incident-in-bangkerohan,-davao-city
Panaji, D. R., Trisasongko, B. H., Susetyo, B., Raimadoya, M. A., & Lees, B. G. (2010). Historical fire detection of tropical forest from NDVI time-series data: a Case study on Jambi, Indonesia. Journal of Mathematical and Fundamental Sciences, 42(1), 49-66.
Perez, E. G., & Ceballos, R. F. (2019). Malaria Incidence in the Philippines: Prediction using the Autoregressive Moving Average Models. International Journal of Engineering and Future Technology (2019), 16(4), 1-10. http://www.ceser.in/ceserp/index.php/IJEFT/article/view/5961
Philippine Information Agency (PIA). (2018 April 10). March is Fire prevention Month. https://pia.gov.ph/features/articles/1019379
Powell, S. G., & Baker, K. R. (2009). Management science: The art of modeling with spreadsheets, Wiley.
Tatoy, A. M. A., & Ceballos, R. F. (2019). Human Immunodeficiency Virus (HIV) Cases in the Philippines: Analysis and Forecasting. JP Journal of Biostatistics, 16(2), 67-77. http://dx.doi.org/10.17654/BS016020067
World Health Organization (WHO). (2018, March 6). Burns. https://www.who.int/en/news-room/fact-sheets/detail/burns
Zhang, D., & Jiang, K. (2012). Application of data mining techniques in the analysis of fire incidents. Procedia Engineering, 43, 250-256. https://doi.org/10.1016/j.proeng.2012.08.043
Zhanli, M. (2012). Disastrous forecasting of fire accidents in assembly occupancies. Energy procedia, 16, 1899-1903. https://doi.org/10.1016/j.egypro.2012.01.290
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Published
2020-12-29
How to Cite
Villa, M. M., & Ceballos, R. F. (2020). Analysis and Forecasting of Fire incidence in Davao City. Recoletos Multidisciplinary Research Journal, 8(2), 35–50. https://doi.org/10.32871/rmrj2008.02.03
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