

PhD Projects
Student-led research advancing disaster management
Current PhD Students
Our current PhD students bring a diverse set of skills and experiences to the team. We are committed to pursuing cutting-edge research in our respective fields and advancing the frontiers of knowledge. Each member of our team is deeply immersed in their own field of study, but we also share a common commitment to collaboration and cross-disciplinary exploration.

Nilani Algiriyage
MSc in Computer Science
University of Moratuwa, Sri Lanka
BSC in Management and Information Technology
Supervisors: Raj Prasanna, Emma Hudson-Doyle, Kristin Stock, David Johnston
Multi-source multimodal deep learning to improve situation awareness:
An application of emergency traffic management
Nilani's PhD research project focuses on using deep learning to fuse multiple modalities of data, including text, visual and audio, in real-time to provide structured information for disaster response. The research aims to improve situation awareness for emergency responders by leveraging the increasing amount of data generated by people during disasters, including data sharing through social media applications. The project is implementing a multimodal deep learning framework in the specific application area of Emergency Traffic Management, using traffic-related CCTV footage obtained through a data sharing agreement with Christchurch City Council.
Papers and presentations
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DEES: a real-time system for event extraction from disaster-related web text
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Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review
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Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage
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Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison
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Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images

Chanthujan Chandrakumar
BSc in Engineering, Electrical, Electronics and Communications Engineering
University of Moratuwa, Sri Lanka
Supervisors: Raj Prasanna, Max Stephens, Caroline Holden, Marion Tan
An eco-system of low-cost ground motion sensors toward Earthquake Early Warning System using P-waves
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.
Papers and presentations

Alfredo Jaramillo Velez
Master in Oceanography
Universidad de Las Plamas de Gran Canaria
Degree in Environmental Engineering
University of Medellin, Colombia
Supervisors: Raj Prasanna, Sam McColl,
Saskia de Vilder, Carol Stewart, Marion Tan
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

Danuka Ravishan
BSc Hons in Electronic and Telecommunication Engineering
University of Moratuwa, Sri Lanka
Supervisors: Raj Prasanna
Reducing false alerts - Dynamic range estimation using machine learning techniques for earthquake early warning
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.
Completed PhD Students
CRISiSLab also has brought PhD students through to completion. These highly accomplished individuals have successfully completed their PhD studies and are now making their mark in various fields. As a team, we are proud of their academic achievements and the knowledge and skills gained through our research.

Marion Lara Tan, PhD
Year graduated: 2020
PhD Thesis: Usability of disaster apps: understanding the perspectives of the public as end-users
Publications from the PhD Project
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Modified Usability Framework for Disaster Apps: A Qualitative Thematic Analysis of User Reviews
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Understanding end-users’ perspectives: Towards developing usability guidelines for disaster apps
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Usability factors influencing the continuance intention of disaster apps: A mixed-methods study
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Mobile applications in crisis informatics literature: A systematic review

Sara Harrison, PhD
Hazard and Risk Management Scientist at GNS Science
Year graduated: 2022
PhD Thesis: Exploring the data needs and sources for severe weather impact forecasts and warnings
Publications from the PhD Project

Syed Yasir Imtiaz, PhD
