Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    ‘All Four Engines Have Failed’: A qualitative study of the health impacts, reactions and behaviours of passengers and crew onboard flight BA009 which flew through a volcanic ash cloud in 1982
    (Elsevier Ltd, 2025-06-15) Meach R; Horwell CJ; de Terte I
    This study investigated the experiences, health impacts and behaviours of passengers and crew onboard British Airways flight BA009 which flew through a volcanic ash cloud from Mount Galunggung, Indonesia, in 1982. In addition to secondary data sources, including a book published by one of the passengers, 18 semi-structured interviews were completed (14 passengers, 2 flight crew and 2 cabin crew) which were video recorded and transcribed verbatim. Data were analysed using reflexive thematic analysis to examine the experiences, behaviours, and actions of those onboard, and the health impacts of exposure to volcanic emissions. Our analysis identified five key themes which explain how people onboard flight BA009 responded: 1) Responsibility, 2) Airmanship and prior knowledge of aviation, 3) Upbringing and cultural background, 4) Faith and 5) Behaviour of the crew. Our study found few physical health impacts associated with the exposure to the ‘smoke’ and, despite individual cases of distress, there was no mass panic onboard the aircraft. Our findings highlight valuable information on passenger and crew behaviour in aviation crises, the risks of volcanic ash clouds to aviation, and have practical implications for aviation disaster management, planning and communication.
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    Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review
    (Springer Nature, 2022-01) Algiriyage N; Prasanna R; Stock K; Doyle EEH; Johnston D
    Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.