Abstract—Disaster preparedness and disaster management plan is one of the main concerns of the government to reduce the damage in human lives and economic loss. As a response, the researchers decided to develop a data-driven architectural framework for LGUs in Disaster Preparedness and Management System. The researchers used descriptive research method to obtain data and information in identifying the attributes needed in employing disaster preparedness and management plan for data mining processes. Qualitative research method was also used to develop the architectural framework to be designed from the identified attributes in disaster preparedness and management plan and to test the viability of the designed architectural framework in disaster preparedness and management system. Respondents of this study are the Municipal Disaster Risk Reduction Management Council (MDRRMC), Department of Agriculture (DA), Municipal Engineering Office (MEO), Municipal Social Welfare and Development (MSWD), and the Local Government Units (LGUs). Based on the assessment of the user (highest scale is 5), the developed architectural framework has weighted mean of 4.58 in terms of efficiency, effectiveness and impact which means that this will serve as a significant tool for decision making to help the LGUs to have a concrete, effective and efficient disaster preparedness and management plan based on their needs to reduce vulnerability in human lives, infrastructure and agriculture.
Index Terms—Architectural framework, data mining, disaster, disaster management plan, disaster preparedness.
The authors are with Camarines Norte State College, Camarines Norte State College, and University of Cordilleras, Philippines (e-mail: firstname.lastname@example.org, email@example.com, and firstname.lastname@example.org).
Cite: Rosemarie T. Bigueras, Jocelyn O. Torio, and Thelma D. Palaoag, "A Data-Driven Architectural Framework for LGUs in Disaster Preparedness and Management System," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 256-261, 2018.