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

城市化水平的提高和气候变化使城市地表水管理成为一个主要关切领域。在英国，有1400万条路边水沟，这些水沟支撑着245,000英里的道路。当沟壑堵塞时，这会导致洪水。目前，地表排水基础设施的维护人员对该网络的运行方式以及如何维护以保护公民免受洪水风险的了解非常有限。该项目将开发管理日益增加的道路网络传感器所需的统计基础设施。特别是，InTouch在英国各地的沟渠中部署了传感器，以监控英国沟渠网络的状态。由于成本和实际考虑，这些传感器在数量上是有限的，因此只有一小部分给定的主机环境可以被监测.该项目将开发统计方法，以表明何时以及如何重新部署传感器将有利于知识获取。一个最佳的时间和地理搬迁，考虑到空间和时间的依赖性，将增加每单位时间的信息增益相对于一个简单的静态网络实现，并提供更准确的可预测性的问题，以驱动清洁制度，减少洪水。目前还没有统计方法可以应用于这个问题，因此本项目试图开发这些方法。

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.