TY - GEN
T1 - Fire risk zone analysis system using a predictive model for time and resource optimization
AU - Suca, Julio Cesar Cardenas
AU - Medina, DIego Jesus Tapia
AU - Oliden, Daniel Alejandro Subauste
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a predictive solution to optimize the time of analysis for the identification of fire risk areas. This proposal consists of two applications: mobile and web. Both applications have functionalities that allow the user to obtain information about the identified risk areas: (1) Fire Register, (2) Risk Zone Register, (3) Display of available resources, (4) Display of fire risk zones, (5) Consultation of emergencies attended to in the last 24 hours and (6) Notification in the event of proximity to a risk zone. The proposal is described in three sections: (1) the optimization of the current process of analysis and identification of fire risk zones, (2) the functionalities of the web and mobile solution, (3) the implementation of the predictive analysis and (4) the designed technological architecture. The validation of the proposal consisted of two parts (1) Validation of the web application in conjunction with INDECI members, with data and records of fires that we obtained from the institution for the analysis of risk areas and (2) Validation of the mobile application that was conducted with 20 users to identify the time of search for risk areas. The results showed that the time of the analysis process was optimized by 75%, while the search time by 90.6%, which mainly generates a social benefit to the citizens by ensuring the quick and efficient identification of fire risk zones, based on a predictive analysis.
AB - In this paper, we propose a predictive solution to optimize the time of analysis for the identification of fire risk areas. This proposal consists of two applications: mobile and web. Both applications have functionalities that allow the user to obtain information about the identified risk areas: (1) Fire Register, (2) Risk Zone Register, (3) Display of available resources, (4) Display of fire risk zones, (5) Consultation of emergencies attended to in the last 24 hours and (6) Notification in the event of proximity to a risk zone. The proposal is described in three sections: (1) the optimization of the current process of analysis and identification of fire risk zones, (2) the functionalities of the web and mobile solution, (3) the implementation of the predictive analysis and (4) the designed technological architecture. The validation of the proposal consisted of two parts (1) Validation of the web application in conjunction with INDECI members, with data and records of fires that we obtained from the institution for the analysis of risk areas and (2) Validation of the mobile application that was conducted with 20 users to identify the time of search for risk areas. The results showed that the time of the analysis process was optimized by 75%, while the search time by 90.6%, which mainly generates a social benefit to the citizens by ensuring the quick and efficient identification of fire risk zones, based on a predictive analysis.
KW - Disaster
KW - Fire
KW - Geolocation
KW - Predictive
KW - System
UR - https://www.scopus.com/pages/publications/85124390451
U2 - 10.1109/SHIRCON53068.2021.9652390
DO - 10.1109/SHIRCON53068.2021.9652390
M3 - Contribución a la conferencia
AN - SCOPUS:85124390451
T3 - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
BT - Proceedings of the 2021 IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Sciences and Humanities International Research Conference, SHIRCON 2021
Y2 - 17 November 2021 through 19 November 2021
ER -