Using spatial modelling to develop flood risk and climate adaptation capacity metrics for assessing urban community and critical electricity infrastructure vulnerability

Espada Jr., R. and Apan, A. and McDougall, K. (2013) Using spatial modelling to develop flood risk and climate adaptation capacity metrics for assessing urban community and critical electricity infrastructure vulnerability. In: 20th International Congress on Modelling and Simulation (MODSIM 2013): Adapting to Change: The Multiple Roles of Modelling , 1-6 Dec 2013, Adelaide, Australia.

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The aim of this study was to develop a new spatially-explicit analytical approach for urban flood risk assessment and generation of climate adaptation capacity metrics for vulnerability assessment of critical electricity infrastructure.
Using the January 2011 flood in Queensland (Australia) with the core suburbs of Brisbane City as the study area, this study addressed the sufficiency of indicating variables and their suitability for climate risk modelling. A range of geographical variables were analysed using a) high resolution digital elevation modelling and urban morphological characterisation with 3D analysis, b) spatial analysis with fuzzy logic, c) proximity analysis, d) quadrat analysis, e) collect events analysis, f) geospatial autocorrelation techniques with global Moran’s I and Anselin Local Moran's I, and g) hot spot analysis. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a 2-dimension self-organising neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using Bayesian joint conditional probability. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches.
The outputs of the topological cluster analysis showed that 15 out of 22 indicating variables were found sufficient to spatially model the flood risk and climate adaptation capacity metrics. The analyses showed that 214ha (9%) and 255ha (11%) of the study area were very highly impacted by the January 2011 flood as indicated by the very high flood risk metrics and the very low adaptation capacity metrics, respectively. In the electricity network vulnerability assessment, a total count of 72 critical assets (zone supply substations, high voltage switching sites, and pole transformer sites) were found highly vulnerable to flood hazard. The flood damage disrupted electricity supply along 627km and 212km of transmission lines on the north western and south eastern sides of the study area, respectively.
The newly developed spatially-explicit analytical technique, identified in this study as the flood risk-adaptation capacity index/metrics-adaptation strategies (FRACIAS) linkage model, will allow the integration of flood risk and climate adaptation assessments which have been treated separately in the past. As technical support to the Queensland Floods Commission of Inquiry (QFCI) recommendations, this study also provides a tool and identifies adaptation strategies to enable urban communities and the power industry to better prepare and mitigate future flood events.
The tool can also be used to assess the physical vulnerability of other critical assets (e.g. water supply, sewerage, communication, stormwater, roads and rails) to flooding.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright 2013 Modelling and Simulation Society of Australia and New Zealand Inc. Permanent restricted access to published version due to publisher copyright policy.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 09 Dec 2013 23:08
Last Modified: 21 Sep 2018 00:48
Uncontrolled Keywords: flood risk assessment; climate adaptation capacity; geospatial autocorrelation; Bayesian joint conditional probability; self-organising neural network
Fields of Research : 04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040604 Natural Hazards
01 Mathematical Sciences > 0104 Statistics > 010404 Probability Theory
09 Engineering > 0909 Geomatic Engineering > 090903 Geospatial Information Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
12 Built Environment and Design > 1205 Urban and Regional Planning > 120507 Urban Analysis and Development
Socio-Economic Objective: B Economic Development > 85 Energy > 8506 Energy Storage, Distribution and Supply > 850603 Energy Systems Analysis

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