Modern society is heavily dependent on critical infrastructure systems, such as coupled human and natural ecosystems, and these increasingly experience both natural and man-made stressors. Despite their national importance, the complexity of these coupled systems means we do not fully understand how to invest and adapt them to different risks and uncertainties. Utilising advances in data and network-science, this project aims to develop an adaptable approach to managing risk and building resilience.
Explaining the science
We can learn from natural complex systems which have evolved under constant attack and environmental stress. In particular, we draw inspiration from natural ecosystems, where two recent discoveries are relevant.
First, it has been shown that these complex systems can grow in size and complexity whilst retaining resilience. Even without precise knowledge of the complex dynamics, one can still check the stability of a system from the network structure. This has the potential to achieve long-term resilient growth of complex systems.
Second, it was shown that ecosystems can dynamically ‘rewire’ to minimise a cascade of failures. This mechanism can achieve dynamic resilience in the face of unknown risks. Further analysis in this project will focus on network robustness under different attack/failure and recovery scenarios. This naturally occurring distributed intelligence may help us develop connected thinking and build both static and dynamic approaches to resilience, that can adapt under future uncertainty.
The three-year project aims to address the large uncertainties that arise due to chain reactions in interdependent critical ecosystems (CHANCE), with a focus on the rural-urban coupled water networks.
The work will leverage on recent advances in data- and network-science to develop a more holistic and adaptable approach to managing risk and building resilience, which as the potential to improve the safety of society and its interdependent critical infrastructures.
This work could benefit any industry with a significant proportion of networked infrastructure elements, especially water distribution operators and transportation.