Evacuee Pre-needs Assessment Using Decision Tree Algorithm of MDRRMC-Daraga Records
A well-designed pre-needs assessment using decision tree algorithm aids the decision maker in implementing the pre-emptive evacuation of the evacuee in preparation for any disasters in the town of Daraga Albay, Philippines. The pre-needs assessment companies to identify and solve problems of the current and manual process of the disaster risk reduction management. Managing the information resources in pre-needs assessment process is one of the most prominent challenges of the public sectors. Evacuation center is a place or a center that the National Government provides the affected people with the basic human needs including food, water as well as accommodation during or before the disasters or calamity occurs. Detailed assessment was conducted by authorized personnel in the government organization to look at how to improve the chances of the disaster victim with their stay in an evacuation center. Sometimes the National Government overlook the population capacity of the victims that may cause shortages of the reliefs provided during the disaster or a calamity. In this paper, the researcher presents the evacuee pre-needs assessment to enhance the current process in conducting the pre-needs assessment with the aim to design and develop a pre-needs assessment system that shall generate accurate reports from population risk analysis up to the analysis of preparing the pre-needs of the evacuee. And also to establish a strong links with better integration between the various barangays, Municipal Disaster Risk Reduction Council and Municipal Social Welfare Department in the preparation for Evacuation Management Preparedness. Albay has zero casualty goal on every disaster that comes its way, it is important that every BDRRMC report actual and accurate information that are at risk prior to calamity and the MDRRMC will generate report accurately to serve the people in the municipality. As well as, the decision tree algorithm used in finding the pre-needs of the evacuee prior to the calamity or disaster. The researcher opts to design a web application to help the government agencies for disaster risk reduction management to accurately determine the pre-needs and actual list of evacuee per calamity/disaster accordingly.
KeywordsDisaster Pre-needs Evacuee Pre-needs assessment Disaster management Decision tree algorithm
The researcher convey his sincerest gratitude to Almighty God for establishing him to complete the project study for without His continuous blessings and guidance, everything that the researcher endeavour would not have been achieved.
The researcher would also like to express his gratefulness to the following persons whose extended efforts and assistance contributed to the successful completion of this project study:
• To his adviser, Dr. Thelma D. Palaoag, for her full support, expert guidance, insightful discussions and suggestions; and encouragement to finish this study; and
• To the Commission on Higher Education for giving him the opportunity to be part of the CHED Faculty Development Program.
- 1.Inquirer News: IN THE KNOW: deadliest typhoons in the Philippines. Retrieved 20 July 2017, from http://newsinfo.inquirer.net/113673/in-the-know-deadliest-typhoons-in-the-philippines (2011)
- 2.NSW Health Organization: Major evacuation centres: public health considerations. Retrieved 5 July 2017 from http://www1.health.nsw.gov.au/pds/ActivePDSDocuments/GL2011_011.pdf (2016)
- 3.Troy, D.A., Carson, A., Vanderbeek, J., Hutton, A.: Enhancing community-based disaster preparedness with information technology. Retrieved 5 July 2017 from http://www.citeulike.org/user/ctrnet/article/2311614 (2008)
- 4.Shah Alam Khan, M.: Retrieved 5 July 2017 from http://www.emeraldinsight.com/doi/abs/10.1108/09653560810918667 (2008)
- 5.Dai, Q., Zhang, C., Wu, H.: Research of decision tree classification algorithm in data mining. Retrieved 5 July 2017 from http://www.sersc.org/journals/IJDTA/vol9_no5/1.pdf (2016)
- 6.Tu, P.L., Chung, J.Y.: A new decision-tree classification algorithm for machine learning. Retrieved 5 July 2017 from http://ieeexplore.ieee.org/document/246431/ (2002)
- 7.Reddy, C., Vasu, V., Archari, V.: Effective decision tree learning. Retrieved 28 Feb 2017 from https://pdfs.semanticscholar.org/6f2b/a5ed846417967a7758964086f479c57753fa.pdf (2013)