Emergency Flood Planning Using Unmanned Aerial Systems, Mathematics/Engineering – PhD (Funded)

Project Description
Natural disaster such as flooding has significant adverse economic, social and cultural impact on the affected communities. As the number of such emergencies is on hike, and the resources available to manage during post-disaster are limited, it is important first of all to enhance the planning/preparedness level, and thereby to provide efficient means to speed up the recovery operations. The project focuses on using instrumented unmanned aerial systems (UASs) to collect and collate pertinent information about an unfolding flooding disaster. The relative ease with which UASs can be deployed (often hand launched) to assess damage across large areas provides emergency responders with the opportunity to assess the situation quickly, allowing the prioritisation of resources and their effective deployment where they are required.

One aspect of the research will focus on addressing the challenges associated with planning mission of UASs in such (non-ideal) situations allowing to efficiently gather useful information (for example based on LiDAR and Camera sensors) for aiding the decision making during emergency. The research project will also consider how the data collected using the UAS can be combined with accelerated flood inundation models to generate detailed evacuation plans, and to predict the nature and progress of the flooding to improve allocation of emergency resources, build community flood resilience, save lives and reduce economic damage. The forecast/evacuation plans from the flood inundation models further aid the planning the operations of single/multiple UAS more efficiently over a region. Some of the many challenges the research project poses to the researcher are associated with near real time decision making under uncertainty based on the dynamic data received from multiple sensors from UASs, the integration of a flood inundation mathematical model with the mission planner of UASs, optimisation of the operations of the possible multiple resources, etc.

An ideal candidate should have ample background knowledge on numerical methods, basics of optimisation, and basics of programming. A good understanding on autopilot basics of unmanned aerial systems (for e.g. Quadrotors) and LiDAR/Camera sensors and exposure to 3D simulation environment will be an advantage. Exposure to principles of dynamic programming and Bayesian methods will be an added benefit. The potential candidate should be passionate to work with mission planners (for e.g. QGroundcontrol) and UoE proprietary flood inundation mathematical models (for e.g. CADDIS-2D, for which additional external support will be available) and interact with other researchers in the consortia, in UK and India. The potential candidate needs to be passionate to implement different techniques which will be developed during the research in MATLAB/C++/Python environment.