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

Author

Listed:
  • 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
    International Program in Engineering for Bachelor, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan City 32003, Taiwan
    Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan City 32003, Taiwan)

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.

Suggested Citation

  • Ardvin Kester S. Ong & Ferani Eva Zulvia & Yogi Tri Prasetyo, 2022. "“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:679-:d:1020715
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    References listed on IDEAS

    as
    1. Josephine D. German & Anak Agung Ngurah Perwira Redi & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Vince Louis M. Sumera, 2022. "Predicting Factors Affecting Preparedness of Volcanic Eruption for a Sustainable Community: A Case Study in the Philippines," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    2. Junfei Chen & Qian Li & Huimin Wang & Menghua Deng, 2019. "A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China," IJERPH, MDPI, vol. 17(1), pages 1-21, December.
    3. Zhao, Yang & Ni, Qi & Zhou, Ruoxin, 2018. "What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age," International Journal of Information Management, Elsevier, vol. 43(C), pages 342-350.
    Full references (including those not matched with items on IDEAS)

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