@article {bnh-6567, title = {Simulation optimisation for natural hazard risk management}, number = {533}, year = {2020}, month = {01/2020}, institution = {Bushfire \& Natural Hazards CRC}, address = {Melbourne}, abstract = {

In this report, we outline a decision-support framework for natural hazard management that combines the use of simulation and optimisation for exploring what risk reduction measures best achieve management priorities. This framework addressed several needs that practitioners have with regard to risk and adaptation assessment. First, it uses optimisation approaches to screen through and identify those management options that perform best across a number of decision criteria. This is important, as there are a very diverse range of management options which could be combined in an extremely large number of ways to make portfolios of management options and it is difficult to screen through all potential portfolios to shortlist a small number for further consideration. Second, it simulates the effectiveness of management options over long-term planning horizons. This is important, for management options may have long lead-in-times, and/or may have long lifetimes, are not easily/readily changed, and so need to be effective over a broad range of plausible future conditions, which likely include larger populations, increased economic development and climate change. Third, the framework emphasises holistic assessment, wherein the Integrated assessment modelling (IAM) simulates the effect of management options across a number of criteria, therefore allowing practitioners to explore the trade-offs between risk reduction with other community goals, including environmental, social and economic outcomes.


The value of this framework arises from the greater demands that are being placed on planners to effectively manage natural hazard risk, whether from politicians or from the public who have an increasingly reduced appetite for hazard losses. This creates a need for analytic frameworks for exploring how to best manage risk, and this framework addresses this gap.


Through application to a case study, this report shows how the framework is able to increase the effectiveness and efficiency by which IAM can be applied to natural hazard risk management. The role of the optimisation was seen not to be prescriptive, but to enable better exploration of risk management options. The framework was therefore able to provide rich information on the effectiveness of management portfolios by which better risk management plans could be formed.


The case study to which the framework was applied considered coastal flood risk within Greater Adelaide and explored the combinations of zonal exclusion areas along the coast which prevented further development. If unmitigated, coastal risk could increase by 10\% over the next three decades. However, results of the case study application showed that zonal exclusion policies could effectively reduce this growth of coastal risk, however this required excluding further development from 7000 Ha of land along the coast. Saying that, the growth in risk could be limited to 3\% (i.e. a reduction in growth by 60\%), through excluding coastal development from only 1500 Ha.

}, keywords = {natural hazard, optimisation, risk management, simulation}, issn = {533}, author = {Jeffrey Newman and Graeme Dandy and Aaron Zecchin and Hedwig van Delden and Charles Newland and Holger Maier and Graeme Riddell} } @mastersthesis {bnh-6623, title = {Developing a (semi) automatic calibration procedure for cellular automata based land use models}, year = {2018}, month = {01/2018}, school = {University of Adelaide}, address = {Adelaide}, abstract = {

Land-use change models are used to understand the wide-ranging impacts that land-use changes have on a region. Effective modelling of land-use changes must capture multiple, mutually influential drivers. A common framework for modelling land-use changes uses Cellular Automata (CA), which have seen a growth in application driven by the availability of generic modelling platforms, shifting the focus of research about Land-Use Cellular Automata (LUCA) models from development to application, with a particular focus on calibration. Calibration of LUCA models is complex, as land-use change is a path-dependent process with uncertain outcomes captured by a number of model parameters. Of note are LUCA models that use a transition potential, which are traditionally calibrated using a manual approach, a process that is time-consuming and lacks objectivity. Hence, there has been a focus on the development of automatic calibration methods for these types of models. To automate calibration, metrics are used to capture two separate properties of performance: locational agreement, the match of pixels between simulated outputs and the corresponding observed data, and landscape pattern structure, the inferred realism of land-use change processes captured by the difference between the observed and simulated landscape patterns. The primary objective of this research is to develop improved automatic calibration methods for transition potential based LUCA models. There are two common approaches, optimisation-based and process-specific. The major contributions of this body of work are the development of improved versions of each type of approach, and the development of a hybrid method combining the advantages of the two approaches. First, a generic multi-objective optimisation framework for automatic calibration of transition potential LUCA models was developed in Paper 1 (Chapter 2) that allows for the exploration of trade-offs between the model performance objectives. Second, a process-specific semi-automatic calibration method that integrates objective analysis with discursive input to facilitate efficient calibration of neighbourhood rules (the main calibration parameter for this type of model) within a limited computational budget was developed in Paper 2 (Chapter 3). Finally, a generic framework for hybrid automatic calibration, which integrates domain knowledge into a multi-objective optimisation approach, was developed in Paper 3 (Chapter 4). The utility of each method was demonstrated via case study applications, showing promising potential for future applications of LUCA models to support long term planning and policy development.

}, keywords = {automatic callibration, cellular automata, land-use models}, url = {https://digital.library.adelaide.edu.au/dspace/handle/2440/119790}, author = {Charles Newland} } @article {bnh-5133, title = {Empirically derived method and software for semi-automatic calibration of Cellular Automata land-use models}, journal = {Environmental Modelling and Software}, volume = {108}, year = {2018}, month = {10/2018}, pages = {208-239}, chapter = {208}, abstract = {

Land-use change\ models generally include neighbourhood rules to capture the spatial dynamics between different land-uses that drive land-use changes, introducing many parameters that require calibration. We present a process-specific semi-automatic method for calibrating neighbourhood rules that utilises discursive knowledge and\ empirical analysis\ to reduce the complexity of the calibration problem, and efficiently calibrates the remaining interactions with consideration of locational agreement and landscape pattern structure objectives. The approach and software for implementing it are tested on four case studies of major European cities with different physical characteristics and rates of\ urban growth, exploring preferences for different objectives. The approach outperformed benchmark models for both calibration and validation when a balanced objective preference was used. This research demonstrates the utility of process-specific calibration methods, and highlights how process knowledge can be integrated with automatic calibration to make it more efficient.

}, doi = {https://doi.org/10.1016/j.envsoft.2018.07.013}, url = {https://www.sciencedirect.com/science/article/pii/S1364815217313105}, author = {Charles Newland and Aaron Zecchin and Holger Maier and Jeffrey Newman and Hedwig van Delden} } @article {bnh-4336, title = {Multi-objective optimisation framework for calibration of Cellular Automata land-use models}, journal = {Environmental Modelling \& Software}, volume = {100}, year = {2018}, month = {02/2018}, pages = {175-200}, chapter = {175}, abstract = {

Modelling of land-use change plays an important role in many areas of environmental planning. However, land-use change models remain challenging to calibrate, as they contain many sensitive parameters, making the calibration process time-consuming. We present a multi-objective optimisation framework for automatic calibration of Cellular Automata land-use models with multiple dynamic land-use classes. The framework considers objectives related to locational agreement and landscape pattern structure, as well as the inherent stochasticity of land-use models. The framework was tested on the Randstad region in the Netherlands, identifying 77 model parameter sets that generated a Pareto front of optimal trade-off solutions between the objectives. A selection of these parameter sets was assessed further based on heuristic knowledge, evaluating the simulated output maps and parameter values to determine a final calibrated model. This research demonstrates that heuristic knowledge complements the evaluation of land-use models calibrated using formal optimisation methods.

}, doi = {10.1016/j.envsoft.2017.11.012}, url = {https://www.sciencedirect.com/science/article/pii/S1364815217306862}, author = {Charles Newland and Holger Maier and Aaron Zecchin and Jeffrey Newman and Hedwig van Delden} } @article {bnh-5072, title = {Multiple objective optimisation framework for automatic parameter tuning of Cellular Automata land-use models with multiple dynamic land-use classes}, journal = {Environmental Modelling and Software}, volume = {100}, year = {2018}, month = {02/2018}, pages = {175-200}, chapter = {175}, abstract = {

Modelling of land-use change plays an important role in many areas of environmental planning. However, land-use change models remain challenging to calibrate, as they contain many sensitive parameters, making the calibration process time-consuming. We present a multi-objective optimisation framework for automatic calibration of Cellular Automata land-use models with multiple dynamic land-use classes. The framework considers objectives related to locational agreement and landscape pattern structure, as well as the inherent stochasticity of land-use models. The framework was tested on the Randstad region in the Netherlands, identifying 77 model parameter sets that generated a Pareto front of optimal tradeoff solutions between the objectives. A selection of these parameter sets was assessed further based on heuristic knowledge, evaluating the simulated output maps and parameter values to determine a final calibrated model. This research demonstrates that heuristic knowledge complements the evaluation of land-use models calibrated using formal optimisation methods.

}, doi = {https://doi.org/10.1016/j.envsoft.2017.11.012}, url = {https://www.sciencedirect.com/science/article/pii/S1364815217306862?via\%3Dihub}, author = {Charles Newland and Hedwig van Delden and Jeffrey Newman and Aaron Zecchin and Holger Maier} } @article {bnh-3355, title = {Futures Greater Adelaide 2050: An exploration of disaster risk and the future}, number = {243}, year = {2017}, month = {01/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Natural disaster risk is a combination of the natural hazard, exposure\ and vulnerability. As a result when considering future disaster risk and planning to minimise it, the uncertainty and complexity of each factor must be considered. Influencing factors on the three components of risk include political decisions, economic development, technological advancement, demographic changes and changing climate, many of which are mutually influential as well. The uncertainty and complexity that arise from these factors are critical to understand when considering long term disaster risk reduction planning, especially when planning decisions can have long lasting influence and large expense.

In an attempt to characterise, understand and subsequently make better decisions under these conditions the BNHCRC funded project {\textquotedblleft}Decision Support System (DSS) for Assessment of Policy \& Planning Investment Options for Optimal Natural Hazard Mitigation{\textquotedblright}, was initiated. For Greater Adelaide the project looks to develop an integrated spatial DSS to model long term changes in risk and subsequently assist decision makers plan and implement disaster risk reduction policies and investments.\  Incorporated with the development of the prototype software package is a facilitated stakeholder engagement process informing the development and then subsequent use of the system.

In September 2014 the first stage of this process was completed with results documented in Van Delden et al. (2015). The second phase, of which this report documents, incorporated the development of exploratory scenarios\ to better understand relevant uncertainties, develop strategic capacity in decision makers to consider uncertainties impacting on policies and provide a better understanding of the value and use of the developed DSS.

The process looked to discover critical elements relevant to disaster risk reduction\ and consider how they change into the future. As a method for exploring the future, scenarios were developed considering the changes from 2013 to 2050. Five alternate futures for Greater Adelaide were developed by members of SA{\textquoteright}s State Mitigation Advisory Group (SMAG), assisted by the scenarios team at the University of Adelaide and Research Institute for Knowledge Systems. These were subsequently modelled and results of the qualitative and quantitative scenarios will be presented in this report.

}, issn = {243}, author = {Graeme Riddell and Hedwig van Delden and Graeme Dandy and Holger Maier and Aaron Zecchin and Jeffrey Newman and Charles Newland} } @article {bnh-4207, title = {Improved decision support for natural hazard risk reduction: annual project report 2016-17}, number = {321}, year = {2017}, month = {09/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Decision support systems that contain integrated models for the assessment of natural hazard mitigation options are an important component for robust, transparent, and long-term mitigation planning. Integrated modelling of underlying social, environmental, and economic systems is required to take into account system dynamics, and to explore the implications of future changes, such as changes in demographics, land use, economics and climate.\ Consequently, a generic decision support system for the long-term planning for natural disaster risk reduction is being developed as part of the Bushfire and Natural Hazards Cooperative Research Centre.\ 

The project consists of implementing an iterative development and use cycle across three different case studies. This development aspect of this cycle focuses on creating a generic framework for the integration of models to answer policy relevant questions, in this case for improved understanding and reduction of disaster risk. The use process tailors this framework to each of the case study regions, Greater Adelaide, Greater \& Peri-Urban Melbourne and Tasmania.\ 

The focus of 2016-2017 has been on the completion of the first development cycle for Greater Adelaide, with the prototype software to be delivered to end user organisations shortly. Training was also provided to users within the South Australian government and on-going support will be provided to this user group to enable uptake and use within respective departments.\ 

Work has also continued on the Greater \& Peri-urban Melbourne case study with significant process in the data collection and application of the DSS system to the region. Coupled with this has also been the use process of scenario development, with four scenarios for the region currently in draft form prior to follow up sessions in 17-18 to finalise the drafts, quantitatively model the scenarios and look to integrate the exploration of plausible futures to strategy development into organisations.

Similar progress in Tasmania has also been made with the land use model application completed and data collection for the hazard, and exposure modelling ongoing for completion of the first iteration of DSS development by the end of 2017. The participatory use process also saw multiple workshops take place in Tasmania throughout 16/17. These were centred on exploring uncertainties for change in the state, along with collecting data for multiple criteria analysis of risk reduction options, and defining optimisation questions for future analysis.

Other efforts have gone into the publication of journal papers, analysing the current state of literature in the field of decision support for risk reduction planning, and the calibration of land use modelling. Significant effort has also been placed into utilisation activities and exploring future application regions, resulting in a successful NDRP project proposal to apply the DSS in Western Australia.\ 

}, issn = {321}, author = {Holger Maier and Hedwig van Delden and Graeme Riddell and Jeffrey Newman and Aaron Zecchin and Graeme Dandy and Charles Newland} } @article {bnh-3916, title = {Review of literature on decision support systems for natural hazard risk reduction: current status and future research directions}, journal = {Environmental Modelling \& Software}, volume = {96}, year = {2017}, month = {10/2017}, pages = {378-409}, chapter = {378}, abstract = {

Natural hazard risk is largely projected to increase in the future, placing growing responsibility on decision makers to proactively reduce risk. Consequently, decision support systems (DSSs) for natural hazard risk reduction (NHRR) are becoming increasingly important. In order to provide directions for future research in this growing area, a comprehensive classification system for the review of NHRR-DSSs is introduced, including scoping, problem formulation, the analysis framework, user and organisational interaction with the system, user engagement, monitoring and evaluation. A review of 101 papers based on this classification system indicates that most effort has been placed on identifying areas of risk and assessing economic consequences resulting from direct losses. However, less effort has been placed on testing risk-reduction options and considering future changes to risk. Furthermore, there was limited evidence within the reviewed papers on the success of DSSs in practice and whether stakeholders participated in DSS development and use.

}, doi = {10.1016/j.envsoft.2017.06.042}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216311239}, author = {Jeffrey Newman and Holger Maier and Graeme Riddell and Aaron Zecchin and James Daniell and Andreas Schafer and Hedwig van Delden and Bijan Khazai and Michael O{\textquoteright}Flaherty and Charles Newland} } @article {bnh-2925, title = {Natural hazard mitigation decision support system: Annual project report 2015-2016}, number = {178}, year = {2016}, month = {08/2016}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Decision support systems (DSS) that contain integrate models for the assessment of natural hazard mitigation options are an important component of robust, transparent, and long-term mitigation planning. Integrated modelling of underlying social, environmental, and economic systems is required to take into account system dynamics, and to explore the implications of future changes, such as changes in demographics, land use, economics and climate. Consequently, a generic decision support system for the long-term planning for natural disaster risk reduction is being developed through the Bushfire and Natural Hazards Cooperative Research Centre.\ The project consists of implementing an iterative development and use cycle across three different case studies. The development aspect of this cycle focuses on creating a generic framework for the integration of models to answer policy relevant questions, in this case for improved understanding and reduction of disaster risk. The use process tailors this framework to each of three case study regions, Greater Adelaide, Greater \& Peri-Urban Melbourne and Tasmania.

The focus of 2015-2016 has been on the use cycle for Greater Adelaide, with a first prototype of the software application presented to end-users and five exploratory scenarios qualitatively and quantitatively developed that capture policy-relevant uncertainties in the development of Greater Adelaide. The DSS application in its first iteration provides annual expected losses from coastal inundation, riverine flooding, bushfire and earthquake. The entire use process has been driven by several stages of stakeholder engagement involving SA{\textquoteright}s State Mitigation Advisory Group.\ Work has also begun on the Greater \& Peri-urban Melbourne case study with the first stage of stakeholder engagement occurring on the 21st {\textendash} 23rd October 2015. This involved questionnaires, semi-structured interviews and a workshop to collect information on spatial region, hazards, drivers for change, risk reduction options and indicators of interest. Model development followed focussing initially on the central land use model, and data have also been collected for hazard modelling. Further stakeholder work will occur in the second half of this year including presentation of a first iteration of the software, and its integrated use process of exploratory scenario development.\ The first stage of stakeholder engagement for the Tasmania case study occurred on 4th {\textendash} 6th November 2015. Similar information was collected regarding inputs to the system{\textquoteright}s development. The spatial region considered initially excluded the World Heritage and National Park sites on the South West of the island, but this has changed due to fire events over summer 2015/16. Due to this change in scope the DSS development has been delayed, with a first iteration of the software now planned to be completed in 2017.
Other work has also involved the development of specific hazard models by project team members to be integrated within the systems for each of the case studies. These include a riverine flood and coastal inundation model, and a bushfire risk assessment model co-developed with SA{\textquoteright}s Department of Environment, Water \& Natural Resources (DEWNR).

}, issn = {178}, author = {Holger Maier and Hedwig van Delden and Graeme Riddell and Jeffrey Newman and Aaron Zecchin and Graeme Dandy and Charles Newland} } @conference {bnh-2949, title = {A spatial decision support system for natural hazard risk reduction policy assessment and planning}, booktitle = {AFAC16}, year = {2016}, month = {08/2016}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Brisbane}, abstract = {

The challenges facing environmental policymakers grow increasingly complex and uncertain as more factors that impact on their ability to manage the environment and its risks need to be considered. Due to a large number of influencing environmental and anthropogenic factors, natural hazard risk is difficult to estimate accurately, and exaggerated by large uncertainty in future socioeconomic consequences. Furthermore, resources are scarce, and the benefits of risk reduction strategies are often intangible. Consequently, a decision support system assisting managers to understand disaster risk has great advantage for strategic policy assessment and development, and is the focus of this extended abstract.
The spatial decision support system (SDSS) presented is being developed in collaboration with several South Australian government departments and funded by the Bushfire and Natural Hazards CRC. It integrates multiple hazard models with a land use model which includes information on population and building stock to consider long term spatial and temporal dynamics of natural hazard risk. The integrated SDSS operates at a 100m resolution with a time-step of one year and can be used to model 20{\textendash}50 years into the future. Hazards included in the SDSS include riverine flood, coastal inundation, bushfire, heatwave and earthquake. Each is modelled dependent on the relevant physical properties of the hazard and include the impacts of climate change on hydro-meteorological, bushfire and heatwave hazard. The land use model is driven by land use demand (population and jobs), and allocates land accordingly.

}, author = {Graeme Riddell and Holger Maier and Hedwig van Delden and Jeffrey Newman and Aaron Zecchin and Roel Vanhout and James Daniell and Andreas Schafer and Graeme Dandy and Charles Newland} } @conference {bnh-2089, title = {A decision support framework for multi-hazard mitigation planning - non peer reviewed extended abstract}, booktitle = {Adelaide Conference 2015}, year = {2015}, address = {Adelaide, Australia}, abstract = {

Research proceedings from the Bushfire and Natural Hazards CRC \& AFAC Conference in Adelaide, 1-3 September 2015.\ 

}, author = {Jeffrey Newman and Hedwig van Delden and Graeme Riddell and Charles Newland and Aaron Zecchin and Holger Maier and Roel Vanhout and Ed Pikusa and Graeme Dandy} } @article {bnh-2342, title = {Natural hazard mitigation decision support system: Annual project report 2014-2015}, number = {135}, year = {2015}, month = {02/11/2015}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Decision support systems (DSS) that contain integrated models for the assessment of natural hazard mitigation options are an important component for robust, transparent, and long-term mitigation planning.\  Integrated modelling of underlying social, environmental, and economic systems is required to take into account system dynamics, and to explore the implications of future changes, such as changes in demographics, land use and climate.\  Consequently, a generic decision support system for the long-term planning of natural disaster impact mitigation options is being developed as part of the Bushfire and Natural Hazards Cooperative Research Centre.

Throughout 2014-2015, the project team has focussed on the production of a framework for the {\textquoteleft}development{\textquoteright} and {\textquoteleft}use{\textquoteright} of the DSS. The {\textquoteleft}development{\textquoteright} aspects propose a generic framework for the integration of models to bridge the science-policy gap through a collaboration between scientists, end users and IT specialists. The {\textquoteleft}use{\textquoteright} process focusses on the application of the generic DSS to a region of interest; in this project three case study locations are considered including Greater Adelaide, Greater Melbourne and Tasmania. This involves collaboration between modellers, stakeholders and facilitators to customise, calibrate and validate the case-study specific integrated models. Subsequently, the integrated model is used to support a Storyline and Simulation (SAS)[1] approach that attempts to develop scenarios through a participatory process, wherein the DSS helps build and assess these scenarios through an iterative process.

Additional to the production of this overarching framework has been specific model development to ensure appropriate models are available to consider hazard risk within the integrated DSS. Risk models for flooding, coastal inundation and bushfire have been conceptually developed and will be applied to each case study as appropriate. Each model focuses on changing risk spatially and temporally.

The Greater Adelaide DSS is the first case study application of the generic framework, which began with an extensive intial stakeholder engagement phase in September 2014. This involved identification of key stakeholders, questionnaries, interviews and a whole-day workshop at the University of Adelaide on the 18th of September. From there, critical external drivers and uncertainties were identified, along with aspects of risk and indicators of hazard impact and mitigation performance. These factors have subsequently been incorporated within the DSS for Greater Adelaide and a proto-type DSS will be completed in time for further stakeholder engagement around scenario development and DSS use in October/November 2015.

Greater Melbourne and Tasmania have also begun to progress as case studies. Key contacts have been developed in each location, and with the assistance of these contacts, data and model availability have been discussed for each location, along with identification of stakeholders relevant for each location. The first stage of stakeholder engagement for these case studies, as described for Greater Adelaide, will occur in October/November 2015.\ 

}, issn = {135}, author = {Holger Maier and Hedwig van Delden and Jeffrey Newman and Aaron Zecchin and Graeme Dandy and Graeme Riddell and Charles Newland and Michael O{\textquoteright}Flaherty} } @article {bnh-1520, title = {Literature Review on Decision Support Systems for Optimising Long-term Natural Hazard Mitigation Policy and Project Portfolios}, year = {2014}, abstract = {

In this report, literature pertinent to the development of natural hazard mitigation decision support systems is reviewed across four areas:

1. Evidence for the increased frequency of natural hazards and the benefits of mitigation are reviewed.\ \ 

2. Mitigation planning is reviewed, with the presentation of mitigation planning frameworks and classifications of mitigation options.

3. The decision support system (DSS) literature is surveyed to understand current research thrusts in this field.\ \ 

4. The development of emergency management DSSs since the early 1980s is surveyed, with a particular focus on DSSs for mitigation.\ 

Consequently, this report concludes that the development of decision support systems that (i) use state of the art methodologies, (ii) incorporate the use of optimisation within a multihazard approach, (iii) consider changes in climate, demographics, land use and economics, and (iv) assess land-use related mitigation measures in addition to structural and management measures, is both important and novel.

}, author = {Jeffrey Newman and Holger Maier and Hedwig van Delden and Aaron Zecchin and Graeme Dandy and Graeme Riddell and Charles Newland} }