The measurement of rainfall in urban environments is a crucial task, with the gathered results playing a key part in flood hazard mitigation, the monitoring and control of pollution, and water resource management. However, there has been a noted decline in the positioning of rain gauges in these environments, due to a combination of cost issues, and the difficulties of recording accurate data, due to the variable urban environment. These difficulties range from water running off of buildings, to vandalism, with the under catchment due to wind, being the largest contributor to errors. It is for these reasons that there is a need to study, develop and assess cheap and novel rain gauges suited to operate in urban environments.
This PhD project is concerned with developing a new technique based on image recognition to estimate rainfall intensity and volume. A rainfall simulator will be built to generate synthetic rainfall, and a range of cameras and sensors will be used to capture the different attributes of rainfall. Image recognition techniques will be used to analyse the pictures and videos, and a numerical model will be built to model the rainfall based on inputs from the cameras and sensors.
The successful candidate is expected to have a first class degree in Civil Engineering or a related discipline, preferably with a good knowledge of Water engineering. Applicants should also have a strong understanding of principles of photography and a good knowledge of pattern recognition and image analysis. Familiarity with computer programming is essential to this project.
Informal enquires can be sent to Dr Soroosh Sharifi ([email protected]
) and in the first instance should contain a covering letter and a CV
For excellent applicants (very good first degree), there is the potential for funding for Home / EU students that will cover fees at the current Home / EU student rate and a stipend. Overseas students are welcome to apply but should note that they will be required to be either completely self-funding, or to make up the difference between Home and Overseas fees.