Development of Fire Report Management Portal with Mapping of Fire Hotspot, Data Mining, and Prescriptions of Fire Prevention Activities

Balahadia, Francis F.; Lagman, Ace C.; Moraga, Shirley D.; Mangaba, Joel B.

doi:10.1109/ismac.2019.8836151

Cited by 1 publication

(2 citation statements)

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“…Entries in the dataset were first grouped according to the fire alert level, then 30% were randomly selected from each alert level. These entries comprised the training set while the remaining 70% was used as the testing set which was also done from the previous study (Balahadia, et al, 2019). Several data analysis algorithms were tested to determine the best classifier model based on accuracy, f-measure, and precision, as shown in Table 2.…”

Section: Interpretation/evaluationmentioning

confidence: 99%

“…In other countries, researchers employ spatiotemporal analysis methods to extract patterns from data of fire incidents, the results of which are used in creating fire prediction models (Bringula & Balahadia, 2018). Asgary, Ghaffari, and Levy (2010) noted that studies were successful in providing insights about spatiotemporal patterns of fire incidents, which were useful in planning fire prevention and response management (e.g., Špatenková & Virrantaus, 2013;Balahadia & Trillanes,2017;Balahadia, et al, 2019;Yao & Zhang, 2016;Wang, et al, 2011). Other studies of fire incidents using prediction and data mining approach were Karouni, et al, (2014), Raghuwanshi (2016), Wang, et al (2017), Stojanova, et al (2006), and Zhang & Jiang (2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Spatiotemporal Analysis and Knowledge Discovery for Databases in the Bureau of Fire Protection as Incident Report System: Tool for Improving Fire Services

Balahadia

Vinluan

Gonzales

et al. 2021

IJCSR

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Purpose–This study aims to contribute to the fire research by developing a fire report management system for the BFP that can analyze spatiotemporal attributes of fire and 520apply Knowledge Discovery in Databases (KDD) methods to identify patterns of fire incidents in the city of Manila.Method–The proponents applied the Knowledge Discovery in Databases (KDD) methods for the processing of identifying fire patterns as well as the application of SMOTE, One-Hot Encoding, and Agile Method as Software developmental model. Result–The records obtained from the BFP headquarters in Manila had a total of 3,506 cases during the six years from 2011 to 2016. The accuracy of the Decision Tree classifier model was 95.92%.Using KDD approach, it generated decision rules fire pattern in Manila. Most fire causes fall under the 'Under Investigation' category while Residential-Commercial types of establishments in Intramuros were affected. Lastly, the fire occurred in the mornings, during Sundays when most people are in their homes and the majority of which took place in the Pandacan district.Conclusion–The application of KDD in building a predictive model to be integrated into the system was the major part of this project. The outputs generated by the system can provide material for use in more accurate fire risk assessments, more efficient allocation of fire resources and personnel, and more targeted fire awareness and prevention programs.Recommendation–Future research in this area may include other factors contributing to a higher likelihood of fire incidences such as weather conditions and other geographical attributes of fire-prone locations. Analysis of these and other relevant factors may allow the BFP to gainfurther insights into the causes of fire incidents, which will enable the agency to make the necessary adjustments and changes in their current fire prevention and risk reduction programsPractical Implication–This study provides direct implication for the Bureau of Fire Protection and community through the given insights of the fire activities and the created model of the system that determine Manila's fire patterns that help identify appropriate information about fire activities and preventive measures of fire incidents.

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Section: Interpretation/evaluationmentioning

confidence: 99%