“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network

Ardvin Kester S. Ong (), Ferani Eva Zulvia and Yogi Tri Prasetyo
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Ardvin Kester S. Ong: School of Industrial Engineering and Engineering Management, Mapua University, 658 Muralla Street, Intramuros, Manila 1002, Philippines
Ferani Eva Zulvia: School of Industrial Engineering and Engineering Management, Mapua University, 658 Muralla Street, Intramuros, Manila 1002, Philippines
Yogi Tri Prasetyo: School of Industrial Engineering and Engineering Management, Mapua University, 658 Muralla Street, Intramuros, Manila 1002, Philippines

Sustainability, 2022, vol. 15, issue 1, 1-21

Abstract: Exploring the intention to prepare for mitigation among Filipinos should be considered as the Philippines is a country prone to natural calamities. With frequent earthquakes occurring in the country, “The Big One” has been predicted to damage the livelihood and infrastructure of the capital and surrounding cities. This study aimed to predict the intention to prepare for mitigation (IP) of “The Big One” based on several features using a machine learning algorithm ensemble. This study applied a decision tree, a random forest classifier, and artificial neural network algorithms to classify affecting factors. Data were collected using convenience sampling through a self-administered questionnaire with 683 valid responses. The results of this study and the proposed machine learning-based prediction model could be applied to predict the intention of younger Filipinos to prepare. The experimental results also revealed that the decision tree and the decision tree with random forest classifier showed understanding, perceived vulnerability, and perceived severity as factors highly affecting the IP of “The Big One”. The results of this study could be considered by the government to promote policies and guidelines to enhance the people’s IP for natural disasters. The algorithm could also be utilized and applied to determine factors affecting IP for other natural disasters, even in other countries.

Keywords: earthquake; disaster mitigation; machine learning algorithm; random forest classifier; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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