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Predicting Factors Affecting Preparedness of Volcanic Eruption for a Sustainable Community: A Case Study in the Philippines

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  • Josephine D. German

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Anak Agung Ngurah Perwira Redi

    (Industrial Engineering Department, Sampoerna University, Jakarta 12780, Indonesia)

  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Yogi Tri Prasetyo

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taiwan)

  • Vince Louis M. Sumera

    (Department of Civil Engineering and Geological Engineering, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

Abstract

Volcanic eruption activity across the world has been increasing. The recent eruption of Taal volcano and Mt. Bulusan in the Philippines affected several people due to the lack of resources, awareness, and preparedness activities. Volcanic eruption disrupts the sustainability of a community. This study assessed people’s preparedness for volcanic eruption using a machine learning ensemble. With the high accuracy of prediction from the ensemble of random forest classifier (93%) and ANN (98.86%), it was deduced that media, as a latent variable, presented as the most significant factor affecting preparedness for volcanic eruption. This was evident as the community was urged to find related information about volcanic eruption warnings from media sources. Perceived severity and vulnerability led to very high preparedness, followed by the intention to evacuate. In addition, proximity, subjective norm, and hazard knowledge for volcanic eruption significantly affected people’s preparedness. Control over individual behavior and positive attitude led to a significant effect on preparedness. It could be posited that the government’s effective mitigation and action plan would be adhered to by the people when disasters, such as volcanic eruptions, persist. With the threat of climate change, there is a need to reevaluate behavior and mitigation plans. The findings provide evidence of the community’s resilience and adoption of mitigation and preparedness for a sustainable community. The methodology provided evidence for application in assessing human behavior and prediction of factors affecting preparedness for natural disasters. Finally, the results and findings of this study could be applied and extended to other related natural disasters worldwide.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11329-:d:911045
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    References listed on IDEAS

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    1. Nattakit Yuduang & Ardvin Kester S. Ong & Nicole B. Vista & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Thanat, 2022. "Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    2. Duarte, Paulo & Pinho, José Carlos, 2019. "A mixed methods UTAUT2-based approach to assess mobile health adoption," Journal of Business Research, Elsevier, vol. 102(C), pages 140-150.
    3. 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.
    4. Ardvin Kester S. Ong & Thanatorn Chuenyindee & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Michael N. Young & , 2022. "Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
    5. Jedsada Phengsuwan & Tejal Shah & Nipun Balan Thekkummal & Zhenyu Wen & Rui Sun & Divya Pullarkatt & Hemalatha Thirugnanam & Maneesha Vinodini Ramesh & Graham Morgan & Philip James & Rajiv Ranjan, 2021. "Use of Social Media Data in Disaster Management: A Survey," Future Internet, MDPI, vol. 13(2), pages 1-24, February.
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    3. Yoshiki B. Kurata & Ardvin Kester S. Ong & Christienne Joie C. Andrada & Mariela Nicole S. Manalo & Errol John Aldrie U. Sunga & Alvin Racks Martin A. Uy, 2022. "Factors Affecting Perceived Effectiveness of Multigenerational Management Leadership and Metacognition among Service Industry Companies," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    4. Yoshiki B. Kurata & Ardvin Kester S. Ong & Ranice Ysabelle B. Ang & John Karol F. Angeles & Bianca Danielle C. Bornilla & Justine Lian P. Fabia, 2023. "Factors Affecting Flood Disaster Preparedness and Mitigation in Flood-Prone Areas in the Philippines: An Integration of Protection Motivation Theory and Theory of Planned Behavior," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    5. 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.

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