By Robert Ogie
Hydrological infrastructure such as pumps, floodgates (or sluice gates), dams, embankments, and other flood barriers are invaluable assets used in coastal cities for mitigating flooding. These infrastructure components are often vulnerable to damage or failure due to the impact of floodwaters, thus exacerbating the flood hazards and causing significant loss of life and destruction to property worth billions of dollars. With the increasing frequency and intensity of rainfall and associated floods in coastal cities, it has become crucial to judiciously allocate limited resources for routine maintenance and upgrade of these hydrological infrastructure components in a manner that improves their resilience and minimises their failure during extreme flooding events. Ideally, such resource allocations and investment decisions should be effectively targeted at the most vulnerable components in the hydrological infrastructure system. Hence, there is a growing need worldwide to enhance the understanding of infrastructure vulnerability and to develop key metrics for assessing it. Though a quantitative assessment of vulnerability can point decision makers to the most vulnerable components in the hydrological infrastructure network, this is not a straightforward task that lends itself to a standardised process of finding suitable metrics. In the context of coastal cities situated in developing nations, this task is further complicated by the lack of sufficient data, potentially limiting the range of possible solutions.
Motivated by this problem, I have begun exploring the use of graph theory in identifying the most vulnerable components in a city-wide hydrological infrastructure network. The graph theory method is considered appropriate for this problem because it provides a rigorous mathematical basis for computing vulnerability, using very little data obtainable at the time and allowing for further improvement from the initial results as additional data becomes available in the future. The idea is that if we can represent point features such as hydrological infrastructure (e.g. pumps, sluice gates, dams, embankments, etc.) as network nodes and line features such as waterways (e.g. rivers, streams, canals, etc.) as edges, then we can construct a city-scale spatio-topological network that can be computationally interrogated to reveal the vulnerability ranking of each hydrological infrastructure component based on what the network tells us about their exposure, sensitivity, and resilience to flood hazards.
This research has produced the novel concept of hydrological infrastructure flood vulnerability index (HIFVI), which has been applied to measure and rank the vulnerability of floodgates in one of the most exemplary coastal cities – Jakarta, Indonesia. The results show that the proposed solution is both useful in highlighting the most vulnerable infrastructure components and also providing clues as to what actions can be taken to minimise infrastructure vulnerability. More so, the solution was found to be useful in identifying potential locations within coastal cities where additional infrastructure are required to improve resilience to flooding. This information is vital to decision-making authorities responsible for planning, flood preparedness and priority-based allocation of resources for the maintenance of flood mitigation infrastructure in coastal cities.
During the International Conference on Smart Infrastructure and Construction (ICSIC) that was held in Cambridge, UK from 27 – 29 June 2016, I presented this work to the broader community of researchers and industry players. The paper titled “Vulnerability analysis of hydrological infrastructure to flooding in coastal cities – A graph theory approach” won the best paper award in the Asset Management category. The award also comes with a £100 Amazon voucher. Thanks to the conference organisers, Cambridge Centre for Smart Infrastructure and Construction (CSIC), UK.