This report provides information on the locations and character of active geological faults and folds in Ashburton District. The faults are mapped at a district scale and the information is intended to highlight areas where there is a risk of permanent fault movement at the ground surface, and where more detailed investigations should be done if development is proposed in that area (depending on the potential activity of the fault and the type of development proposed). See Object Overview for background and usage information. Most of the faults and folds identified at the ground surface in Ashburton District are in rural or very sparsely populated areas. In addition, most of the faults have relatively long recurrence intervals (long-term average time between fault movements) in the order of several thousand years. Following the Ministry for the Environment Active Fault Guidelines, normal residential development would be allowed on or near faults with recurrence intervals this long. There are no recommendations associated with this report. The information in the report will be reviewed as required, after the remaining district reports are completed in the region.
Artificial Neural Networks (ANN) as a tool offers opportunities for modeling the inherent complexity and uncertainty associated with socio-environmental systems. This study draws on New Zealand ski
fields (multiple locations) as socio- environmental systems while considering their perceived resilience to low
probability but potential high consequences catastrophic natural events (specifically earthquakes). We gathered
data at several ski fields using a mixed methodology including: geomorphic assessment, qualitative interviews,
and an adaptation of Ozesmi and Ozesmi’s (2003) multi-step fuzzy cognitive mapping (FCM) approach. The data
gathered from FCM are qualitatively condensed, and aggregated to three different participant social groups. The
social groups include ski fields users, ski industry workers, and ski field managers. Both quantitative and
qualitative indices are used to analyze social cognitive maps to identify critical nodes for ANN simulations. The
simulations experiment with auto-associative neural networks for developing adaptive preparation, response and
recovery strategies. Moreover, simulations attempt to identify key priorities for preparation, response, and
recovery for improving resilience to earthquakes in these complex and dynamic environments. The novel mixed
methodology is presented as a means of linking physical and social sciences in high complexity, high uncertainty
socio-environmental systems. Simulation results indicate that participants perceived that increases in Social
Preparation Action, Social Preparation Resources, Social Response Action and Social Response Resources have
a positive benefit in improving the resilience to earthquakes of ski fields’ stakeholders.
