Deep Learning for Surface Water Flood Risk Mapping and Forecasting at Loughborough University
Job Description
Project Rationale
Flooding, a significant global hazard, is becoming more frequent and severe due to climate change. Despite advancements in forecasting, accurate prediction of surface water flooding remains challenging.
Research Aims
This PhD project aims to revolutionise surface water flood forecasting by leveraging cutting-edge AI and machine learning techniques.
Key objectives include:
- Improving Weather Prediction: Developing advanced deep learning models to accurately predict extreme rainfall events.
- Enhancing Flood Modelling: Integrating improved weather forecasts with the high-performance HiPIMS model for real-time, high-resolution flood simulations.
- Quantifying Uncertainty: Analysing and minimizing uncertainties in the forecasting process to improve the reliability of flood predictions.
- Real-time Flood Mapping and Risk Assessment: Demonstrating the effectiveness of the developed system in real-world scenarios.
By addressing these challenges, this research will contribute to more accurate and timely flood warnings, enabling better preparedness and response strategies.
Funding Details
Funding Comment
Tuition fees cover your teaching, assessment, and access to University facilities like the library and IT. You can pay in advance or by instalments. Fees are reviewed annually and may increase.
Additional information
Studentship type – UKRI through Flood-CDT.
The studentship is for 3.5 years and provides a tax-free stipend of £19,237 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.
