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Latest Research « Australia Disaster Management Program

A Geographic Primitive Based Framework for Predicting Propagation of Natural Disasters

This project aims to achieve faster real time computation for predicting the failure time from natural disasters including cyclone induced flood and bushfire. High calculation efficiencies are reached through statistically summarizing simultaneous events spread in geography, into primitives allowing a distributed updating algorithm leading to parallel computing. Geographic primitives are to be identified through offline calculations concerning behaviours of environmental driving forces (e.g. water flow patterns for flood propagation, fuel distribution and wind for bushfire propagation, etc). Real time prediction would start with minimal available data and the prediction will be refined as more data are aggregated through broadband networks where parallel processing on the geographic primitives increases calculation efficiency.

Expected Outputs

  1. A Model is developed for real-time prediction of cyclone induced flood propagation by focusing on:
    • Making real-time prediction faster by parallel processing on identified stochastic systems (GPs).
    • Starting prediction with minimal available data and refining with any data that are subsequently available.
    • Allowing flexibility in data assimilation using a modularized approach.
  2. A Bayesian framework is built to generate a probabilistic prediction combining prior knowledge, including rainfall data statistics and topographical features, with any new precipitation, where the advantages of data oriented and heuristic modelling are combined.
  3. A generalized framework will be developed which acts as a basis for calculating mean first passage times for stochastic propagation on non-homogeneous media with environmental bias.
  4. The framework will be enhanced to calculate mean first passage time for a random spread. This will be tested on a bushfire scenario.

Further information available from here:
http://people.eng.unimelb.edu.au/malkah/Natural_Disasters.html


 

Informing Decisions About Natural Disasters: Flood and Fire

Iconic project funding, jointly with NDRGS and ADMP support

This project is developing new capabilities for improving expert decision-making in the domains of flooding and bushfires, through the smart of new technologies and new data sources. In achieving this aim, the project:

      • has developed and deployed a large environmental sensor network. The network is capable of
      • rapid deployment in a range of environments, generating fine-grained spatial and temporal information
      • about environmental changes over a period of weeks with minimal maintenance.
      • is developing new techniques for integrating fine-grained sensor-data in the network with decision
      • making tools, including innovative visualization tools and traditional coarse-scale modeling systems, such as the Phoenix fire model.

This research provides the ADMP with an early demonstration of how new sensed information can be used as part of a wider platform of information capture and integration for emergency management.

The broader research questions being addressed by this project are contributing to the generation of new scientific and engineering knowledge in the domains of:

      • technology resilience, enabling robust sensor-based data capture to adapt to changing environments;
      • multi-scale information, integrating data from diverse spatial and temporal scales;
      • uncertainty, ensuring systems are tolerant to low-quality sensor data;
      • visualisation, providing new techniques for presenting and interacting with dynamic and multidimensional
      • sensor data; and
      • decision making, helping make sense of large volumes of sensor data.

 

An Intelligent Disaster Decision Support System for Urban Disaster

This research aims to develop a prototype Disaster Decision Support System (DDSS) for urban disasters integrating a smart Geospatial Information Platform (“Platform”) with an advanced optimisation and simulation engine. The DDSS will support an end-to-end process from scenario planning to disaster response and recovery. The platform will perform real-time collection, management, analysis, distribution, and visualisation of information for enhanced situation awareness, aligning the impact of information with its availability. This real-time stream of critical information will populate the optimisation/simulation engine whose goal is to increase the cognitive abilities of decision makers when faced with an urban disaster of large magnitude and uncertainty.

      • A framework for a new generation of disaster management information infrastructure facilitating the integration and interpretation of multi-sourced data and services through inter-agency collaboration.
      • A prototype will be developed which integrates data from multiple sources including volunteered geographic information and has the capability to model multiple interdependent infrastructures in real time.
      • The prototype will be integrated and tested on a wide variety of mobile devices (mobile phones and tablet devices).
      • The prototype will be piloted and tested on two disaster scenarios – a flash flood and a state-wide fire blackout.

Further information available from here: http://csdila.unimelb.edu.au/projects/NDRGS/