PhD: Deep learning for surface water flood risk mapping and forecasting

Loughborough University

Loughborough, UK 🇬🇧

Project details

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:

  • 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.

Background Reading

Brown (2020) How much more climate change is inevitable for the UK? Committee on Climate Change, UK.
White et al. (2019) Flash flooding is a serious threat in the UK. The Conversation.
Ming X, Liang Q, Xia X, Li D, Fowler HJ (2020) Real-time flood forecasting based on a high-performance 2D hydrodynamic model and numerical weather predictions. Water Resources Research, 56, e2019WR025583.

Supervisors

Primary supervisor: Qiuhua Liang

Entry requirements

Our entry requirements are listed using standard UK undergraduate degree classifications i.e. first-class honours, upper second-class honours and lower second-class honours. To learn the equivalent for your country, please choose it from the drop-down below.

Entry requirements for United Kingdom

FLOOD-CDT is multidisciplinary, and we welcome applicants from diverse disciplines, including but not limited to: physical scientists, engineers, mathematicians, life and social scientists.

Students should have an interest in multidisciplinary research, as well as other skills relevant to one or more of the core Research and Training themes within the CDT.

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.

Academic attainment is only one of our criteria for selection; we equally value the ability to work in teams, excitement for research, enthusiasm for the research focus of the CDT and the ability to communicate ideas.

English language requirements

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

How to apply

All applications should be made online. Under the programme name, select Architecture, Building and Civil Engineering. Please quote reference FCDT-24-LU4a application. 

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents:

  • an academic transcript of your undergraduate degree showing the modules studied and marks achieved
  • a copy of your degree certificate, if you have already graduated
  • a copy of your CV
  • a personal statement – this should explain your motivation for studying the programme, any relevant skills and experience you have (through studying or work) for this particular project, and your future career aspirations and how this programme will support you in achieving this. Remember to let your passion for the subject shine through!
  • names of two academic referees

For more information about required documents, learn how to start your research application

Applications close on Sunday 7 July (midnight), with interviews to follow (dates to be confirmed)
The selection criteria will be used by academic schools to help them make a decision on your application.


POSITION TYPE

ORGANIZATION TYPE

EXPERIENCE-LEVEL

DEGREE REQUIRED

You ad could be here!