A video of a presentation by Richard Conlin during the Community Resilience Stream of the 2016 People in Disasters Conference. The presentation is titled, "Resilience, Poverty, and Seismic Culture".The abstract for this presentation reads as follows: A strategy of resilience is built around the recognition that effective emergency response requires community involvement and mobilization. It further recognizes that many of the characteristics that equip communities to respond most effectively to short term emergencies are also characteristics that build strong communities over the long term. Building resilient communities means integrating our approaches to poverty, community engagement, economic development, and housing into a coherent strategy that empowers community members to engage with each other and with other communities. In this way, resilience becomes a complementary concept to sustainability. This requires an asset-based change strategy where external agencies meet communities where they are, in their own space, and use collective impact approaches to work in partnership. This also requires understanding and assessing poverty, including physical, financial, and social capital in their myriad manifestations. Poverty is not exclusively a matter of class. It is a complex subject, and different communities manifest multiple versions of poverty, which must be respected and understood through the asset-based lens. Resilience is a quality of a community and a system, and develops over time as a result of careful analysis of strengths and vulnerabilities and taking actions to increase competencies and reduce risk situations. Resilience requires maintenance and must be developed in a way that includes practicing continuous improvement and adaptation. The characteristics of a resilient community include both physical qualities and 'soft infrastructure', such as community knowledge, resourcefulness, and overall health. This presentation reviews the experience of some earlier disasters, outlines a working model of how emergency response, resilience, and poverty interact and can be addressed in concert, and concludes with a summary of what the 2010 Chilean earthquake tells us about how a 'seismic culture' can function effectively in communities even when government suffers from unexpected shortcomings.
We examined the stratigraphy of alluvial fans formed at the steep range front of the Southern Alps at Te Taho, on the north bank of the Whataroa River in central West Coast, South Island, New Zealand. The range front coincides with the Alpine Fault, an Australian-Pacific plate boundary fault, which produces regular earthquakes. Our study of range front fans revealed aggradation at 100- to 300-year intervals. Radiocarbon ages and soil residence times (SRTs) estimated by a quantitative profile development index allowed us to elucidate the characteristics of four episodes of aggradation since 1000 CE. We postulate a repeating mode of fan behaviour (fan response cycle [FRC]) linked to earthquake cycles via earthquake-triggered landslides. FRCs are characterised by short response time (aggradation followed by incision) and a long phase when channels are entrenched and fan surfaces are stable (persistence time). Currently, the Te Taho and Whataroa River fans are in the latter phase. The four episodes of fan building we determined from an OxCal sequence model correlate to Alpine Fault earthquakes (or other subsidiary events) and support prior landscape evolution studies indicating ≥M7.5 earthquakes as the main driver of episodic sedimentation. Our findings are consistent with other historic non-earthquake events on the West Coast but indicate faster responses than other earthquake sites in New Zealand and elsewhere where rainfall and stream gradients (the basis for stream power) are lower. Judging from the thickness of fan deposits and the short response times, we conclude that pastoral farming (current land-use) on the fans and probably across much of the Whataroa River fan would be impossible for several decades after a major earthquake. The sustainability of regional tourism and agriculture is at risk, more so because of the vulnerability of the single through road in the region (State Highway 6).
The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.
