School of Computing and Engineering

Intelligent monitoring and Flood Prediction for Real Time Control of Combined Sewer Systems with Rainfall Radar Data

Primary supervisor: Dr Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering.

Spills from combined sewer systems during extreme rainfall events can cause regulatory failures which may result in negative environmental impacts on receiving water bodies. Hence, flood control management of combined sewer infrastructure is of paramount importance for water companies and development of a decision support system (DSS) for timely prediction of flood and real time control in combined sewer overflow (CSO) structures sounds to be necessary. The need for such a DSS is undeniable as it can be highly beneficial for proactive management of flood control. In addition, making a physically based models for sewerage infrastructure in most cases is a tedious task and data-demanding. 

Research goal

This PhD programme aims to develop a decision support system (DSS) for real time control, intelligent monitoring and prediction of water flow in the sewage chambers. The DSS include a number of key modules including data collection, data processing unit/analyser, flood prediction using rainfall radar data from satellites and real time control for flood mitigation measures. Data processing module would allow to analyse the raw data and provide timely predictions of CSO failures in advance and evaluate some mitigating measures in real time and identify the best alternative to be implemented. The DSS can be used as an early warning system for water authorities to take appropriate measures before overflowing and spillage in sewer systems. The DSS utilises an artificial intelligence system to accurately and timely predict the water depth and alert the decision makers if any flooding is going to happen in the following hours based on rainfall radar data. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Engineering (e.g. civil, water, weather and environment), Natural Resources or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data, and conclude with the validation of a proposed solution through a real-life case study.

Besides basic knowledge in water systems, background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (e.g. MATLAB, VBA or MS Visual Studio C#), water supply and urban drainage systems.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For general enquiries about the application process visit the Graduate School pages.

Questions regarding academic aspects of the project should be directed to