PhD Projects

An eco-system of low-cost ground motion sensors toward Earthquake Early Warning System using P-waves

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This PhD research project addresses knowledge gaps in earthquake early warning systems (EEWS), particularly in implementing decentralised processing, ground motion-based algorithms, and using different types of low-cost micro-electro-mechanical systems (MEMS)-based sensors. The project aims to develop a P-wave-based decentralised low-cost EEWS using a ground motion-based algorithm and an EEWS that can work with different types of MEMS-based sensors. The research will be conducted in New Zealand, where there is a lack of a nationwide EEWS.

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Papers and presentations

• Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review 

• Algorithms for detecting P-waves and earthquake magnitude estimation: Initial literature review findings

Using citizen science data as an approach to make low-cost and safe monitoring programmes in landslide zones: Cape Kidnappers and Taranaki North Cliffs case studies

 

This PhD research project explores the potential of citizen science data to enhance traditional landslide monitoring techniques that can be costly and pose safety risks to personnel. By studying alternative data sources, the project will develop a framework to integrate citizen science initiatives for effective landslide monitoring. The study will focus on two case sites with active rockfall hazards, Cape Kidnappers and Taranaki North, to reduce risks and improve safety for the community and

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Design and Optimisation of Machine Learning Models for Resource-Constrained Edge Devices in On-site Earthquake Early Warning Systems

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The proposed study aims to reduce false alerts of earthquake early warning (EEW) by developing a machine learning-based method that dynamically adjusts the alerting area based on predicted ground shaking intensity. The study will collect earthquake data, explore different ML techniques and Ground Motion Prediction Equations (GMPEs), and model an EEW system. By incorporating historical seismic data and ML, the study aims to improve real-time predictions and increase the robustness of future EEW systems. The proposed system offers benefits such as decreased warning time and adaptability to different ground conditions.

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Crowdsourced and AI-driven approach for impact-based flood forecasts and warnings in New Zealand​

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My research focuses on enabling impact-based flood early warnings in New Zealand by addressing key modelling challenges. A major barrier to such warnings is the computational demand of conventional phsyics-based numerical simulations. To overcome this, I develop AI-driven models for rapid, high-resolution flood inundation forecasting. This work sits at the intersection of data science, natural hazards, and emergency management, and involves integrating dynamic dataset such as hydrological and topographical data to model flood hazards and predict their impacts at fine spatial scales, including the household level. To support the dissemination of these localised forecasts, I am also developing a mobile app based platform. The app provides users with real-time, location-specific impact forecasts and enables crowdsourced reporting of local flood observations. This two-way data flow not only helps validate and improve model performance but also empowers communities with timely and actionable information.