Spatial optimisation for resilient infrastructure services

Abstract

Ph. D. Thesis.Infrastructure networks provide crucial services to the functioning of human settlements. Extreme weather events, especially flooding, can lead to disruption or complete loss of these crucial infrastructure services, which can have significant impacts on people’s health and wellbeing, as well as being costly to repair. Urban areas concentrate infrastructure and people, and are consequently particularly sensitive to disruptions due to natural (and human-made) disasters. Flooding alone constituted 47% of all weather-related disasters between 1995 and 2015, causing enormous loss of lives and economic damages. Climate change is projected to further exacerbate the impacts that natural disasters have on cities. Choices about where to site infrastructure have a significant impact on the impacts of extreme weather events. For example, investments in flood risk management have typically focussed on prioritising interventions to protect people, houses and businesses. Protection of infrastructure services has either been a bonus benefit of flood defence protection of property, or been implemented by individual infrastructure operators. Spatial planning is a key process to influence the distribution of people and activities over broad spatial scales. However, decision-making processes to locate infrastructure services does not typically consider resilience issues at broad spatial scales which can lead to inefficient use of resources. Moreover, spatial planning typically requires consideration of multiple, sometimes competing, objectives with solutions that are not readily tractable. Balancing multiple trade-offs in spatial planning with multiple variables at high spatial resolution is computationally demanding. This research has developed a new framework for multi-objective Pareto-optimal location-allocation problems solving. The RAO (Resource Allocation Optimisation) framework developed here is a heuristic approach that makes use of a Genetic Algorithm (GA) to produce Pareto-optimal spatial plans that balance a typical tradeoff in spatial planning: the maximisation of accessibility of a given infrastructure service vs the minimisation of the costs of providing that service. The method is applied to two case studies: (i) Storage of temporary flood defences, and (ii) Location of healthcare facilities. The RAO is first applied to a flood risk management case study in the Humber Estuary, UK, to optimise the strategic allocation of storing space for emergency resources (like temporary flood barriers, portable generators, pumps etc.) by maximising the accessibility of warehouses (i.e. minimising travel times from storing locations to deployment sites) and minimising costs. The evaluation of costs involves both capital and operational costs such as the length of temporary defences needed, storage site locations, number of lorries and personnel to enable their deployment, and maintenance costs. A baseline is tested against a number of scenarios, including a flood disrupting road network and thereby deployment operations, as well as variable infrastructure and land use costs, different transportation and deployment strategies and changing the priority of protecting different critical infrastructures. Key findings show investment in strategically located warehouses decreases deployment time across the whole region by several hours, while prioritising the protection of the infrastructure assets serving larger shares of population can cut costs by 30%. Moreover, the analysis of the ensemble of all scenarios provides crucial insights for spatial planners. For example, storage sites in Hull or Hedon, and in the areas of Withernsea and Drax are robust choices under all scenarios. Meanwhile, the Humber Bridge is shown to play a crucial role in enabling regional coverage of temporary barriers. The second case study shows how emergency response strategies can be enhanced by optimal allocation of healthcare facilities at a regional scale. The RAO framework allocates healthcare facilities in Northland (New Zealand) balancing the trade-off between maximisation of accessibility (i.e. minimisation of travel times between households and GP clinics) and minimisation of costs (i.e. number of clinics and doctors). Results show how c.80% of Northland’s population lives within a 20 minutes drive from the closest GP, but this can be increased to 90% with strategic investment and relocation of doctors and clinics. By accounting for flood and landslide risk, the RAO is used to identify strategies that improve accessibility to healthcare services by up to 5% even during extreme events (when compared to the current business as usual service accessibility). Application to these two problems demonstrates that the RAO framework can identify optimal strategies to deploy finite resources to maximise the resilience of infrastructure services. Moreover, it provides an analytical appreciation of the sensitivity between planning tradeoffs and therefore the overall robustness of a strategy to uncertainty. The method is consequently of benefit to local authorities, infrastructure operators and agencies responsible for disaster management. Following successful application to regional scale case studies, it is recommended that future work scale the analysis to consider resource allocation to protect infrastructure at a national scaleEngineering and Physical Sciences Research Counci