Monitoring of landslide hazards with wireless sensor networks and machine learning, Geography and Computer Science - PhD (Funded)

Landslides are a major hazard along coasts and in mountain regions in the UK and globally, causing disruption, fatalities and severe economic loss. Landslides can also propagate downslope in the form of debris flows and even interact with floods to cause a cascade of hazards. Landslides and related hazard cascades are increasing under climate change and increasing population pressure (Froude and Petley, 2018). This makes monitoring and early warning of slope instability and landslides increasingly vital to mitigate their impacts.

A range of smart sensors are being developed that can track debris movement through the landslide hazard cascade as part of a Wireless Sensor Network (WSN) (Dini et al., 2021). In the same way that smart watches can characterise different movements we make based on algorithms developed using machine learning, similar techniques can be used characterise different sensor movements and develop warnings of hazardous slope movement. This is a major objective of the SENSUM project: Smart SENSing of landscapes Undergoing hazardous hydrogeomorphic Movement, on which this PhD project will build.

The overarching aim of this PhD is to help develop effective real time monitoring of a range of landslide hazards in coastal and mountainous environments of the UK and France. It will utilise existing data collected by sensors for a range of sites, including Lyme Regis and Isle of White in the UK and Harmalière landslide in France. Drone and video camera footage will be collected to validate sensor movements. Machine learning methods will be used to characterise boulder movements and train sensors to detect hazardous movement using the collected sensing data and drone images, thereby working towards developing effective early warning of hazards.

The student will join a vibrant geomorphology community at the University of Exeter, joining Dr Bennett’s research group as well as the wider CCoRD research group. The student will gain valuable skills in sensor technology through Dr Kyle Roskilly and industry sponsor Copernicus Technologies and machine learning through Dr Luo.

The student will receive adequate training in various sensor technologies used in the project. The student will be encouraged to attend relevant training workshops and opportunities as they arise and will be able to attend at least one international conference and a number of national conferences over the course of the project.

This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £17,668 per year tax-free stipend. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.

The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence on 1st March 2023.

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.

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