Coupled human and natural critical ecosystems

Modern society is heavily dependent on critical infrastructure systems. Many of these systems are coupled human and natural ecosystems, and increasingly experience both natural and man-made stressors.

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Complex Network Representation of a Critical Infrastructure

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.

This 3-year project funded by the Alan Turing Institute-Lloyd’s Register Foundation Programme in Data-Centric Engineering aims to address the large uncertainties that arise due to chain reactions occurring in our interdependent critical ecosystems, with a focus on the rural-urban coupled water networks. This 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, and this shall improve the safety of the society and its interdependent critical infrastructures.

 

We can learn from natural complex systems which have evolved under constant predation 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 cascade failures. This mechanism can achieve dynamic resilience in the face of unknown risks.

Further analysis will focus on network robustness under different attack/failure and recovery scenarios. This naturally occurring distributed intelligence in adapting under future uncertainty may help us develop connected thinking and build both static and dynamic approaches to resilience.

Part of The Alan Turing Institute-Lloyd’s Register Foundation Programme for Data-Centric Engineering

Funded by :

Lloyds R

Publications and Software

W. Zhao, T. Beach, and Y. Rezgui, “Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions,” IEEE Transactions on Systems, MAN and Cybernetics, vol. 46, 2016.

M. Herrera, E. Abraham, and I. Stoianov, “A graph-theoretic framework for assessing the resilience of sectorised water distribution networks,” Water Resources Management, vol. 30, 2016.

“A. Wilson, “Boltzmann, Lotka and Volterra and Spatial Structural Evolution: an Integrated Methodology for Some Dynamical Systems,” Royal Society Interface, 2008

S. Kartakis, E. Abraham, J. McCann, “Waterbox: a Testbed for Monitoring and Controlling Smart Water Networks,” ACM Int. Workshop on Cyber Physical Systems for Smart Water Networks, 2015

P. Mucha et al., “Community Structure in Time-Dependent, Multiscale, and Multiplex Networks”, Science, vol. 328, 2010

M. Williams, M. Musolesi, “Spatio-Temporal Networks: Reachability, Centrality and Robustness,” Royal Society Open Science, 2016