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NETWORK / 19 / OCTOBER 2019 INDUSTRY INSIGHT Using big data to reduce vegetation risk Shane Brunker, chief technology offi cer at NM Group, discusses the importance of vegetation management and how it can be used to improve network operators storm resilience. W ith winter fast approach- ing, the risk of power outages due to storm activity escalates. To help meet this chal- lenge NM Group have launched a big data vegetation modelling service, to predict where future tree falls are likely to occur. The aim being for networks to be able to use predictive analysis to put better protections in place. Developed as part of a joint research project with Durham University, this new service is called Vegeta- tion Analytics. In order to mitigate against storm-related tree failure, networks need to precisely iden- tify and remove or manage trees with the potential to strike overhead lines. Current inspection technology can highlight trees in close proximity to powerlines. Yet for most distribution networks with a large expo- sure to vegetation, the number of potential fall-in hazards might number tens or even hundreds of thousands of trees. Clearly, when the number of possible threats is unmanageably high there is a pressing need for data-driven decision making to prioritise ƒ nite resources. Vegetation Analytics is a data service that processes vast amounts of data to understand trends and predict future vegetation threats. It does this through the inclusion of both historic data and cur- rent network information. Because of the data volumes involved, machine learn- ing processes are used to rapidly assess variables which have the potential to cause or in… uence tree fall. The analysis shows which factors have a strong positive correla- tion with historic tree strikes on a speciƒ c network. Variables include tree type and forest structure, topography, soil type and drainage, historic weather and existing tree management records. Previous testing on utility networks has shown an accuracy of up to 80 per cent in the predictive ability of this modelling technique. Once a localised predictive model has less, one of the limitations of the existing business-as-usual approach is that it only considers a single snapshot in time to as- sess network risks. Historical information can also tell you a lot about what happened and why, and aid in making new, accurate predictions for the future. By looking at the data more holistically, it allows operators to more eŒ ectively plan against storms and optimise OPEX spend for future years. For more information on Vegetation Analytics visit www.nmgroup.com been established, the system applies this to a virtual 3D replica of the entire utility forest. Additional techniques can further improve the model's predictive capabilities. For example, fall risk can be combined with modelled tree fall direction and the degree of overstrike to aid in reƒ ning inspection and removal planning. As a result networks can ensure they proactively manage these locations in a prioritised manner. Geospatial inspection techniques, such as LiDAR, are already being used successfully by UK DNOs for identifying vegetation threats. Nonethe-