Artificial Intelligence
This research theme focuses on using artificial intelligence (AI) to improve disaster, emergency, and crisis management. The goal is to develop AI-based tools to facilitate more effective decision-making, faster response times, and better outcomes for affected communities. The research will focus on using AI to understand hazards and risks associated with disasters and emergencies by analysing large datasets from various sources.
AI can help identify potential hazards, assess their severity, and predict their impact on communities. AI can also be used for response efforts by providing situational awareness and supporting decision-making by emergency responders. Additionally, AI can analyse sentiment data to understand how affected communities feel and to provide more targeted and effective communication during response efforts.
Understanding usage behaviour on the New Zealand COVID19 Tracer App
Description: The project investigates why people in New Zealand used or did not use the NZ COVID Tracer App, which was a tool designed to help with contact tracing during the COVID-19 pandemic period of 2020-2022. Despite the app being downloaded by up to 3 million people, its usage was lower, with only around 2 million devices had Bluetooth active and less than 1.2 million active devices a day. The project uses large data sets from user reviews, social media comments, and Ministry of Health data to gain insights into user experience and behaviour, using machine learning and deep learning to understand sentiments. The study provides a more comprehensive understanding of the app's performance and highlight areas for improvement.
Led by: Marion Tan
Status: Completed
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Led by: Raj Prasanna
Status: Ongoing
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Led by: Raj Prasanna
Status: Ongoing
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Led by: Raj Prasanna
Status: Ongoing
Projects
Publications
Algiriyage, N., Prasanna, R., Stock, K., Doyle, E.E.H., Johnston, D. (2023). DEES: a real-time system for event extraction from disaster-related web text. Social Network Analysis and Mining. https://doi.org/10.1007/s13278-022-01007-2
Algiriyage, N., Prasanna, R., Stock, K., Doyle, E.E.H., Johnston, D. (2022). Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review. SN Computer Science. https://doi.org/10.1007/s42979-021-00971-4
Tan, M.L., Senaweera, O., Gunwardana, A., Rasith, M., Suaib, M., Shanthakumar, T., Hisham, M. (2022). New Zealand COVID Tracer App: Understanding Usage and User Sentiments. Proceedings of the ISCRAM Asia Pacific Conference 2022. https://idl.iscram.org/files/marionlaratan/2023/2483_MarionLaraTan_etal2023.pdf
