Modelling Geographical and Temporal Factors For Decision Support in Evolving Sensor Placement

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Increasing levels of urbanization and climate change have placed managing surface water in cities asan area of major concern. In the UK, there are 14M roadside gullies and these support 245,000 milesof road. When gullies become blocked this leads to flooding. At present the maintainers of thesurface water drainage infrastructure have a very limited understanding of the way in which thisnetwork operates and how it should be maintained to protect citizens from the risks of flooding.This project will develop the statistical infrastructure required to manage the increasingsensorfication of our road network. In particular, InTouch have deployed sensors in gullies acrossthe UK to monitor the state of the UK gully network. These sensors are limited in number due to costand practical considerations thus only a small portion of a given host environment may bemonitored. This project will develop statistical methods to indicate when and how redeployment ofsensors would benefit knowledge acquisition. An optimal time and geographical relocation, takinginto account spatial and temporal dependency, will increase the information gain per unit timerelative to that achieved by a simple static network and provide more accurate predictability ofproblems to drive cleaning regimes and reduce flooding. There are currently no statistical methodsthat can be applied to this problem and thus this project seeks to develop these.