iD-FRe2S develops the combined use of Unmanned Aerial Vehicles (UAV) and Artificial Intelligence (AI) to better identify the impacts of flooding (fluvial, pluvial and sewer) and facilitate clean up and recovery.
The PhD will be based at Cranfield University under the supervision of Dr. Monica Rivas Casado and Dr. Yadira Bajon Fernandez. A cross-Theme multidisciplinary project between the Water Theme (Dr. Bajon Fernandez) and the Environment and Agrifood Theme (Dr. Rivas Casado).
Flood recovery response in urban areas depends on the type of flood (e.g., pluvial, fluvial, groundwater) and the type of water (i.e., clean, grey or black water) affecting the area. In certain areas medium and high intensity rainfall events can cause combined clean and dirty water drainage systems to surcharge and cause contamination of river and flood waters. In addition, high water levels in rivers can cause hydraulic locking of sewage treatment works causing a backup within the associated drainage systems again causing discharges of sewage contaminated water into streets and properties. This contaminated water poses significant risks to human health and requires intensive post event cleaning activities.
Flood response services require detailed and dynamic information to determine the extent and continued risk of each flood and water type to better target the nature and deployment of recovery resources.
The data is critical to rescue and response teams, but also infrastructure management organisations responding to protect critical infrastructure such as electricity supply, gas supply and water as well as critical roads that are the escape routes for people and their belongings. The appropriate dynamic choices not only have the potential to save lives and infrastructure but also may improve community recovery and enhance the recovery of businesses driving localised economic recovery.
Current remote sensing methodologies used for that purpose are curtailed by several limitations:
• Spatio-temporal coverage may not be available for the required zone and period,
• Optical imagery cannot provide information if there is low cloud cover, and
• Satellite (SAR) data, which can penetrate cloud cover, has an oblique viewing angle which makes it difficult to discriminate the water signature from other urban features.
• Recent advances in the use of emerging technologies carried out at Cranfield University [EP/P02839X/1; EP/N010329/1; NE/N020316/1; NE/P018890/1] have enabled the development of novel monitoring solutions to address this gap in knowledge
This project will contribute to address current gaps in knowledge via the extensive use and testing of remote sensing emerging technologies and novel machine learning methods.
The aim of this project is to develop a combined UAV-AI based framework to identify the impacts of flooding from different sources and facilitate clean up and recovery activities. This will be achieved through the following four objectives:
– Objective 1. To develop a set of algorithms for the detection of flood impact to infrastructure (electricity supply, gas supply, water networks, water treatment plants and transport networks, amongst others) from high resolution UAV imagery.
– Objective 2. To quantify the robustness of key indicators for the identification of damage caused by different types of flood to critical infrastructure.
– Objective 3. To optimise decision making for a set of scenarios that look at maximising the saving of lives, community recovery, localised economic recovery and infrastructure protection.
– Objective 4. To develop a set of operational guidelines (and associated risk communication strategy) for flood managers and first emergency responders based on the results from objectives 1 to 3.
This PhD is sponsored by EPSRC, Atkins and Cranfield University. EPSRC is the British Research Council that funds research in engineering and the physical sciences. Atkins was established in 1938 by Sir William Atkins (London). Originally, the company specialised in civil and structural design. Nowadays, Atkins offers a varied range of services, including planning, engineering sciences, architecture and project management. More information about Atkins can be found here.
What will the student gain from experience (transferable skills, employability): The successful applicant will gain the necessary skills to be able to interpret and analyse high-resolution drone imagery within the context of flood impact to infrastructure using machine learning techniques. The successful applicant will have access to a wide range of training activities already in place for PhD students at Cranfield University plus additional training offered via other channels. The student will work in close relationship with the industrial sponsor.
At a glance
- Application deadline15 May 2020
- Award type(s)PhD
- Start date08 Jun 2020
- Duration of award4 years
- EligibilityUK, EU, Rest of World
- Reference numberSWEE0098
Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit candidates with knowledge in flood modelling, remote sensing, machine learning, geography and a desire to work in the field. Candidates should also have a good knowledge of mathematics and strong programming ability in a high-level language (preferably C/C++, Java, Python). Prior experience in computer vision, image processing and/or machine learning is a plus although not essential.
Sponsored by EPSRC, Atkins and Cranfield University, this studentship will provide a bursary of up to £18,000 plus fees for four years.
To be eligible for this funding, applicants must be a UK national. We require that applicants are under no restrictions regarding how long they can stay in the UK i.e. have no visa restrictions or applicant has “settled status” and has been “ordinarily resident” in the UK for 3 years prior to start of studies and has not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals). Due to funding restrictions all EU nationals are eligible to receive a fees-only award if they do not have “settled status” in the UK. If you are an international or EU national applicant interested in this vacancy, please discuss potential options with the contact provided.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.
How to apply
If you are eligible to apply for the PhD, please complete the online PhD application form stating the reference No. SWEE0098