Search

found 36 results

Research papers, University of Canterbury Library

Abstract. Natural (e.g., earthquake, flood, wildfires) and human-made (e.g., terrorism, civil strife) disasters are inevitable, can cause extensive disruption, and produce chronic and disabling psychological injuries leading to formal diagnoses (e.g., post-traumatic stress disorder [PTSD]). Following natural disasters of earthquake (Christchurch, Aotearoa/New Zealand, 2010–11) and flood (Calgary, Canada, 2013), controlled research showed statistically and clinically significant reductions in psychological distress for survivors who consumed minerals and vitamins (micronutrients) in the following months. Following a mass shooting in Christchurch (March 15, 2019), where a gunman entered mosques during Friday prayers and killed and injured many people, micronutrients were offered to survivors as a clinical service based on translational science principles and adapted to be culturally appropriate. In this first translational science study in the area of nutrition and disasters, clinical results were reported for 24 clients who completed the Impact of Event Scale – Revised (IES-R), the Depression Anxiety Stress Scales (DASS), and the Modified-Clinical Global Impression (M-CGI-I). The findings clearly replicated prior controlled research. The IES-R Cohen’s d ESs were 1.1 (earthquake), 1.2 (flood), and 1.13 (massacre). Effect sizes (ESs) for the DASS subscales were also consistently positive across all three events. The M-CGI-I identified 58% of the survivors as “responders” (i.e., self-reported as “much” to “very much” improved), in line with those reported in the earthquake (42%) and flood (57%) randomized controlled trials, and PTSD risk reduced from 75% to 17%. Given ease of use and large ESs, this evidence supports the routine use of micronutrients by disaster survivors as part of governmental response.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.

Research papers, University of Canterbury Library

This study analyses the success and limitations of the recovery process following the 2010–11 earthquake sequence in Christchurch, New Zealand. Data were obtained from in-depth interviews with 32 relocated households in Christchurch, and from a review of recovery policies implemented by the government. A top-down approach to disaster recovery was evident, with the creation of multiple government agencies and processes that made grassroots input into decision-making difficult. Although insurance proceeds enabled the repair and rebuilding of many dwellings, the complexity and adversarial nature of the claim procedures also impaired recovery. Householders’ perceptions of recovery reflected key aspects of their post-earthquake experiences (e.g. the housing offer they received, and the negotiations involved), and the outcomes of their relocation (including the value of the new home, their subjective well-being, and lifestyle after relocation). Protracted insurance negotiations, unfair offers and hardships in post-earthquake life were major challenges to recovery. Less-thanfavourable recovery experiences also transformed patterns of trust in local communities, as relocated householders came to doubt both the government and private insurance companies’ ability to successfully manage a disaster. At the same time, many relocated households expressed trust in their neighbours and communities. This study illuminates how government policies influence disaster recovery while also suggesting a need to reconsider centralised, top-down approaches to managing recovery.