PhD Studentship: Deep Learning for Surface Water Flood Risk Mapping and Forecasting

Rationale:

Flooding – the most wide-spread natural hazard – affects every country and region of the world. Flood risk is expected to increase due to climate change, as evidenced by recent recurring UK summer and winter floods. The UK climate projections (UKCP18) suggest a >10% increase in heavy rainfall by 2050, with much of this “very likely” to fall in a short period of time [1], causing more severe surface water flooding. This type of flooding threatens more UK people and properties than any other; 3.2 million properties in England alone.

Reliable forecasting and early warning can improve preparations, response and recovery, but rapid onset and localised extent make observing and predicting surface water flooding from intense rainfall technically challenging, and our ability to provide reliable, detailed forecasts remains limited [2]. We recently made a significant contribution by developing a new high-performance hydrodynamic system to forecast surface water flooding across an entire catchment at unprecedented resolution [3]. But the latest developments in AI and data analytics technologies have not yet sufficiently exploited to advance operational surface water flood forecasting; uncertainties in different components a forecasting system, e.g. numerical weather predictions and flood dynamics modelling, need to be better understood, quantified and minimised.

Methodology:

The aim of this exciting PhD project is to harness the latest developments in high-performance computing and deep learning (DL) technologies to address some of the key technical challenges, and finally demonstrate a DL-enabled system for mapping, risk assessment and real-time forecasting of surface water flooding from intense rainfall.

Entry requirements:

Applicants must already have, or expect to shortly graduate with, a very good undergraduate degree or Master’s degree (at least a UK 2:1 honours degree) – or an equivalent international qualification from a high ranking university – in a relevant
subject.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Funding information:

Studentship type – UKRI through Flood-CDT (flood-cdt.ac.uk)
The studentship is for 3.5 years and provides a tax-free stipend of £19,237 per annum plus university tuition fees at the UK rate.

How to apply:

All applications must be made online via the above ‘Apply’ button. Under programme name, select 'Centre for Doctoral Training in Resilient Flood Futures'. Please quote the advertised reference number: FCDT-25-LU7-2 in your application. This PhD is being advertised as part of the Centre for Doctoral Training for Resilient Flood Futures (FLOOD-CDT). Further details about FLOOD-CDT can be found at https://flood-cdt.ac.uk. Please note that your application will be assessed upon: (1) Motivation and Career Aspirations; (2) Potential & Intellectual Excellence; (3) Suitability for specific project and (4) Fit to FLOOD-CDT. So please familiarise yourself with the FLOOD-CDT before applying. During the application process candidates will need to upload:

• A 1 page statement of your research interests in flooding and FLOOD-CDT and your rationale for your choice of project
• A curriculum vitae giving details of your academic record and stating your research interests
• Academic transcripts and degree certificates (translated if not in English)
• An IELTS/TOEFL certificate, if applicable

You are encouraged to contact potential supervisors by email to discuss project specific aspects of the proposed prior to submitting your application. If you have any general questions, please contact floodcdt@soton.ac.uk

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