Deep learning for surface water flood risk mapping and forecasting (Ref: FCDT-25-LU7)

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:

• Develop physis-informed DL models to integrate rainfall observations from different sources and identify conditions associated with very extreme rainfall from the outputs of convection-permitting numerical weather models, and then emulate such numerical weather predictions to create reliable real time forecasts or large ensembles.
• Integrate the improved weather forecasts with the Loughborough in-house High-Performance Integrated hydrodynamic Modelling System (HiPIMS) to forecast in real time the surface water flooding process at a meter-level resolution for assessing flood impact/risk on individual buildings/objects. HiPIMS will be ML-enabled to support rapid ensemble forecasting.
• Design and perform systematic numerical experiments to better understand and quantify the uncertainties in different steps, their interaction and propagation through the entire flood forecasting procedure, and interpret their implication on the final flood forecasting and risk products.
• Demonstrate the system for real-time flood mapping, risk assessment and probability forecasting in a selected case study site.

94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

Supervisors

• Primary Supervisor: Professor Qiuhua Liang
• Secondary supervisor: Jinghua Jiang

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a relevant subject such as geography, economics, or engineering. A relevant master’s degree and/or experience is desirable. 

EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

How to apply

All applications must be made online and must include a completed studentship application form (instead of a personal statement) and a two-page research proposal based on the project description describing how you would approach the project and what methods you would use. Under programme name, please select 'Architecture, Building and Civil Engineering (Built Environment)'. Please quote reference number FCDT-25-LU7.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents including an up-to-date CV, but a personal statement is not required.

ABCE will use these selection criteria to make a decision on your application.

Apply now