Qualification Type: | PhD |
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Location: | Loughborough |
Funding for: | UK Students, EU Students, International Students |
Funding amount: | A tax-free stipend of £18,622 per annum plus tuition fees at the UK rate. |
Hours: | Full Time |
Placed On: | 8th March 2024 |
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Closes: | 8th April 2024 |
Reference: | FCDT-24-LU4 |
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. The project will deliver the following key research tasks:
This project is part of the NERC funded Flood-CDT studentship competition. For more information, please visit the Flood-CDT website.
Please see this PhD project’s dedicated webpage for more information via the above ‘Apply’ button.
Additional 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 £18,622 per annum plus tuition fees at the UK rate. Excellent International candidates are eligible for a full international fee waiver however due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates.
For details on what documents you will be required to submit for this, please see the dedicated project page, available via the Apply button.
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