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PhD Studentship: Monitoring of Landslide Hazards with Wireless Sensor Networks and Machine Learning, NERC GW4+ DTP PhD Studentship for 2022 Entry, PhD in Geography

University of Exeter - Department of Geography

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students
Funding amount: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22)
Hours: Full Time
Placed On: 21st October 2021
Closes: 10th January 2022
Reference: 4244

Funding: Also entitled to a research budget of £11,000 for an international conference, lab, field and research expenses and a training budget of £3,250 for specialist training courses and expenses.

Location: Streatham Campus, University of Exeter, Exeter, Devon

Lead Supervisor: Dr Georgie Bennett, University of Exeter

Additional Supervisors

Dr Claire Earlie, Cardiff University

Dr Chunbo Luo, University of Exeter

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory.  Its aims to provide a broad training and designed to train tomorrow’s leaders in earth and environmental science http://nercgw4plus.ac.uk/

Project Background

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 are able to 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.

Project Aims and Methods 

The overarching aim of this project is to help develop effective real time monitoring of a range of landslide hazards in coastal and mountainous environments of the UK and Switzerland. It will utilise existing data collected by sensors on the SENSUM project for a range of sites in the UK, including Lyme Regis and Isle of White, and potentially set up new sites in Scotland and/or Switzerland. 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.

In addition to the field measurements, the sensors will be tested at the BGS landslide laboratory. This includes the usage of the BGS Debris Flow Flume. This experimental setup allows the simulation of debris flow characteristics observed in field under the controlled lab environment. It is possible to control initial and boundary conditions. e.g. initiation, erosion, rough base, slope angles, grain size.

New study sites could include the Rest and Be Thankful in Scotland and the Illgraben in Switzerland (Badoux et al., 2009), where sensors embedded in boulders along the debris flow channel would help to understand processes of debris flow initiation and flow dynamics and help with early warning.

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