Skip to main content

Funding and scholarships for students

Award details

Monitoring of landslide hazards with wireless sensor networks and machine learning, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Geography Ref: 4244

About the award


Lead Supervisor

Dr Georgie Bennett, University of Exeter, Geography

Additional Supervisors

Dr Claire Earlie, Cardiff University, School of Earth and Environmental Sciences

Dr Chunbo Luo, University of Exeter, Computer Science

Location: Streathum Campus, University of Exeter, Exeter, Devon.

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.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An stipend for 3.5 years (currently £15,609 p.a. for 2021/22) in line with UK Research and Innovation rates
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £3,250 for specialist training courses and expenses

Project details

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. 

Candidate requirements

The candidate should have a degree in Geography, Environmental Science or a related discipline. Knowledge of landslide processes and coastal erosion will be valuable. Any experience in data analysis, GIS, remote sensing and fieldwork would also be helpful. 

Project partners 

The student will join a vibrant geomorphology community at the University of Exeter, joining Dr Bennett’s research group of several postdocs and PhD students as well as the wider CCoRD research group. The student will gain valuable machine learning skills through Dr Luo. At BGS the student will have the chance to use the BGS landslide laboratory, like the debris flow flume test and will have discussions with landslide experts about landslides processes and landslide understanding. 


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. 

Background reading and references

Badoux, A., Graf, C., Rhyner, J., Kuntner, R., McArdell, B.W., (2009) A debris-flow alarm system for the Alpine Illgraben catchment: design and performance. Nat Hazards 

Dini, B., Bennett, G.L., Franco, A.M.A., Whitworth, M.R.Z, Cook, K.L., Senn, A., Reynolds, J.M. (2021) Development of smart boulders to monitor mass movements via the Internet of Things: A pilot study in Nepal. Earth Surf. Dynam., 9, 295-315, 2021  

Froude, M.J., and Petley, D.N., (2018) Global fatal landslide occurrence from 2004 to 2016, Nat Hazards Earth Syst Sci, 28, 2161-2181. 

Useful links

For information relating to the research project please contact the lead Supervisor via email at


NERC GW4+ DTP studentships are open to UK and Irish nationals who, if successful in their applications, will receive a full studentship including payment of university tuition fees at the home fees rate.

A limited number of full studentships are also available to international students which are defined as EU (excluding Irish nationals), EEA, Swiss and all other non-UK nationals.  For further details please see the NERC GW4+ website.

Those not meeting the nationality and residency requirements to be treated as a ‘home’ student may apply for a limited number of full studentships for international students. Although international students are usually charged a higher tuition fee rate than ‘home’ students, those international students offered a NERC GW4+ Doctoral Training Partnership full studentship starting in 2022 will only be charged the ‘home’ tuition fee rate (which will be covered by the studentship). 

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. More information on this is available from the universities you are applying to (contact details are provided in the project description that you are interested in.

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.



Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the project


How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • CV
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English, please see the entry requirements for details.
  • Two references

Reference information
You will be asked to submit two references as part of the application process.  If you are not able to upload  your reference documents with your application please ensure you provide details of your referees.  If you provide contact details of referees only, we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the, 7 days prior to the interview dates. Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is 1600 hours GMT Friday 10 January 2022. Interviews will be held between 28 February and 4 March 2022.  For more information about the NERC GW4+ DPT please visit

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the lead supervisor.

Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  • the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
  • administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.


Application deadline:10th January 2022
Value:£15,609 per annum for 2021-2022
Duration of award:per year
Contact: PGR Enquiries