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

Increasing levels of urbanization and climate change have placed managing surface water in cities as
an area of major concern. In the UK, there are 14M roadside gullies and these support 245,000 miles
of road. When gullies become blocked this leads to flooding. At present the maintainers of the
surface water drainage infrastructure have a very limited understanding of the way in which this
network 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 increasing
sensorfication of our road network. In particular, InTouch have deployed sensors in gullies across
the UK to monitor the state of the UK gully network. These sensors are limited in number due to cost
and practical considerations thus only a small portion of a given host environment may be
monitored. This project will develop statistical methods to indicate when and how redeployment of
sensors would benefit knowledge acquisition. An optimal time and geographical relocation, taking
into account spatial and temporal dependency, will increase the information gain per unit time
relative to that achieved by a simple static network and provide more accurate predictability of
problems to drive cleaning regimes and reduce flooding. There are currently no statistical methods
that can be applied to this problem and thus this project seeks to develop these.