Machine Learning for Community Well-Being: Identifying Factors Affecting Resilience in Disaster-Prone Regions
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Sultana, Nasrin
Kamal, Md Sarwar
Ip, Ryan Ho Leung
Bewong, Michael
Islam, Md Zahidul
Rahman, Azizur
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Springer Science and Business Media LLC
Abstract
This paper presents a machine learning approach to identify factors affecting community well-being in New South Wales (NSW), Australia, based on regional well-being survey (RWS) data. The study focuses on NSW, which has recently experienced multiple types of disasters, and utilizes machine learning techniques to identify indicators that can help prevent the decline of community well-being using community users’ responses to survey data. However, obtaining labelled data for this type of study is challenging, so the paper introduces a recommender-based approach that utilizes randomly generated instances, eliminating the need for labels to identify factors. The primary objective is to investigate the factors that affect the current level of community well-being. The study identifies these factors using a recommender-based model and highlights the adaptability of this model across regional well-being survey datasets, indicating its potential to make significant contributions to the field of disaster resilience and enhance community well-being.
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4404 Development studies, Community well-being, Machine learning, Correlation coefficient, Resilience, Exploratory descriptive analysis
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International Journal of Community Well-Being, ISSN: 2524-5295 (Print); 2524-5309 (Online), Springer Science and Business Media LLC, 9(3). doi: 10.1007/s42413-026-00305-3
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Creative Commons Attribution CC BY 4.0
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Except where otherwise noted, this item's license is described as Creative Commons Attribution CC BY 4.0