Explainable Predictive AI Models for Environmental Impact of Offshore Wind Farms

Project Description:

This project is closed to new applicants

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience; a partnership between the Universities of Durham, Hull, Loughborough and Sheffield. The successful applicant will undertake six-month of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at Hull.

Offshore wind is vital to the UK’s transition to net zero, but as the industry scales, understanding and managing its environmental and socio-economic impacts becomes increasingly important. This PhD will develop explainable predictive AI models to assess and forecast how offshore wind farms influence marine ecosystems, seabed mobility, and local industries such as fishing.

Unlike traditional “black-box” AI, these models will focus on explainability, ensuring that predictions are transparent and trustworthy for regulators, developers, and local communities. The research will integrate diverse datasets from ecological monitoring, geospatial surveys, and socio-economic sources (including DEFRA and MMO datasets) to build models that capture both environmental and human dimensions of offshore wind.

By combining machine learning with physics-informed modelling, the project will deliver predictive tools that remain consistent with scientific principles while being interpretable to non-specialists. This will allow stakeholders to better anticipate biodiversity changes, manage seabed risks, and understand socio-economic trade-offs.

The student will join a dynamic research environment at Hull and Loughborough Universities, working closely with other PhDs in the cluster on sustainable offshore wind. They will also engage with industry partners and policymakers, ensuring that research outputs have direct real-world impact.

Training and development

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull for the new student intake, drawing on the expertise and facilities of all four academic partners. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship.

The successful candidate will gain cutting-edge expertise in AI, sustainability, and stakeholder engagement—skills that are in high demand in both academia and industry.

If you have any queries about this project, please contact Dr Xinhui Ma, xinhui.ma@hull.ac.uk 

You may also address queries about the CDT to auracdt@hull.ac.uk.

Watch our short video to hear from Aura CDT students, academics and industry partners:

References & Further Reading 

Díaz, H., Guedes Soares, C., & Bhattacharjee, J. (2021). Machine learning techniques for environmental impact assessment: A systematic review. Environmental Modelling & Software, 145, 105188. 

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 93. 

Nnajiofor, C.A., Eyo, D.E., Adegbite, A.O., Abdullahi, I., Odoguje, E.W.S., Folorunsho, F.E. and Adeyeye, A.A., 2024. Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability. environment, 24, p.25. 

Astolfi, D., De Caro, F. and Vaccaro, A., 2023. Condition monitoring of wind turbine systems by explainable artificial intelligence techniques. Sensors, 23(12), p.5376.