Queensland Fire & Emergency Services Futures Service Demand Forecasting Model

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This case was submitted as part of the Call for Innovations, an annual partnership initiative between OPSI and the UAE Mohammed Bin Rashid Center for Government Innovation (MBRCGI)

By applying state-of-the-art data science approaches to both Queensland Fire & Emergency Services (QFES) data (internal, public, or otherwise), the project forecasted the likelihood of major hazards (e.g. cyclones, fire) which drive the service demands for QFES. Using these likelihoods, the project generated 1,000 statewide 10-year service demand scenarios. Each service demand scenario was assessed regarding proposed capital and operational investment plans. With this innovation, data rather than tradition now drives investment decisions.

Innovation Summary

Innovation Overview

Historically Queensland Fire and Emergency Services (QFES) have used the opinion and experience of their senior executive in consultation with key stakeholders to determine where and when to make capital and operational investments in its evolving emergency response capability. In this digital age there are now decades of available data which can be used to forecast and model the likely future demands upon Queensland's Fire and Emergency Services in the coming years. This valuable input has not yet been deployed to QFES's long term strategic planning.

In response, QFES worked with Queensland's Department of Housing & Public Works to develop a joint emergency service demand forecast based on the objective data. Data was sourced from both department's holdings, Queensland government Open Data, and other 3rd party data sources to inform sophisticated machine learning forecasts of hazard probabilities (e.g. flood, cyclone, fire, road crash, rescue etc) and evolving exposures (e.g. people, assets) over the coming 10 years. This stream, the first of 3, in this project used self-organising maps, Bayesian algorithms and regression techniques to generate the daily probabilities in each location over the coming 10 years.

Based on these probabilities, a data science team (comprising the 2nd of the 3 streams) generated 1000 potential emergency service demand scenarios using stochastic randomisation processes. Each service demand scenario generated 10 years of statewide service demands including incident type, location, severity and duration. Service demands included structure fires, bushfires, swift water rescues, entrapment rescues, road crashes, false alarms, animal rescues, requests for assistance and many other categories of QFES emergency responses. The 1000 10 year scenarios included some scenarios where seemingly everything goes wrong at once and others where there seems little to do in between relatively easy demands on QFES capabilty. Most scenarios are somewhere in between these two extremes.

The third and final stream used Python and in-memory SQLite to (i) generate an artificially intelligent demand response dispatch engine and (ii) simulate the servicing of the 10 years of service demands (including travel time and response escalation). With access to all of the resources available to QFES across the state, combined with the future planned changes to capability (either new, enhanced or decommissioned), the AI dispatches the closest available resource to each of the service demands, tracking which service locations exhaust their resources and when. Across the entire 10 year scenario, a count of local service location and network-wide resource exhaustions is maintained as well as the proportion of times that response time targets were achieved (e.g. x% of responses achieved within 14 minutes). This was repeated for each of the 1000 scenarios to identify which locations are the most frequently unable to service their local service demands. Differing investment plans are also assessed to identify which investments provide the greatest benefit to the community in terms of reduced local resource exhaustion and increased likelihood of adequate response times.

Once all 1000 scenarios are simulated and assessed against each of the candidate investment plans, an assessment is made regarding which of the investment plans is most value for money for QFES and the Queensland community. The project has left QFES with the capability to continue assessing further investment options against the generated scenarios and also the ability to re-generate the scenario with differing assumptions regarding the impact of investment in PPRR (prevention, preparation, response & recovery) and the impacts of changing environmental factors like climate change. A range of geospatial animations have been produced for the more interesting snippets of scenarios (e.g. where network-wide resource exhaustions occurred) and to demonstrate which incident types are varying their duration, frequency and/or severity by season, by location and over the 10 year period.

The results are being used to determine which locations throughout the state are most likely to produce the biggest improvement in emergency response per dollar invested. The timing of these investments is also being assessed to ensure the most beneficial investments are being made at the earliest time feasible.

The model itself will undergo continual improvement. Some of the assumptions of the model were based on scant sets of data, whilst other simplifications were taken to save on computational overhead. As more data and computational resource is made available, the model can evolve to become a closer and closer reflection of reality. Further, post-hoc assessment of how the scenarios reflected subsequent actual events will allow further refinements to the model to further improve its accuracy and utility.

Innovation Description

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Year: 2017
Level of government: Regional/State government

Status:

  • Identifying or Discovering Problems or Opportunities - learning where and how an innovative response is needed
  • Generating Ideas or Designing Solutions - finding and filtering ideas to respond to the problem or opportunity
  • Developing Proposals - turning ideas into business cases that can be assessed and acted on
  • Implementation - making the innovation happen

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