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SMART Resilient Communities
Technology is transforming the communities we live in. How can we harness technology to help build our resilience to natural hazards and disasters?
Analysis of Felt Detailed ‘Final Comments’ for the 2016 Kaikōura earthquake
S.Neupane, P.Inch, J.S. Becker, L. Vinnell, T. Goded, R. Prasanna 2024 Analysis of Felt Detailed ‘Final Comments’ for the 2016 Kaikōura earthquake Conference: New Zealand…
Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review
Low-cost ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust earthquake early warning system.
Bridging gaps between disaster risk reduction and drowning prevention
L. Le Dé 2024 Bridging gaps between disaster risk reduction and drowning prevention International Journal of Disaster Risk Reduction, 106, DOI: 10.1016/j.ijdrr.2024.104445
Adapting PLUM: Earthquake Early Warning with Node-Level Processing in New Zealand
C.Chandrakumar, M.L. Tan, C. Holden, M.T. Stephens, R. Prasanna 2024 Adapting PLUM: Earthquake Early Warning with Node-Level Processing in New Zealand ISCRAM Proceedings, 21.
Creating a ‘planning emergency levels of service’ framework – a silver bullet, or something useful for target practice?
Considerations for the development of Planning Emergency Levels of Service (PELOS) frameworks.
Performance analysis of P-wave detection algorithms for a community-engaged earthquake early warning system – a case study of the 2022 M5.8 Cook Strait earthquake
Which algorithm is best for detecting P-waves generated by earthquakes and generating earthquake early warnings?
Estimating S-Wave intensity for early earthquake warning in New Zealand: Leveraging the first 3 seconds of P-wave intensity
C. Chandrakumar, M.L. Tan, C. Holden, M.Stephens, A.Punchihewa, R. Prasanna 2024 Estimating S-Wave intensity for early earthquake warning in New Zealand: Leveraging the first 3…
Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images
Selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images to estimate traffic flow.