Explainable AI for UK agricultural land use decision-making

Agricultural land use dynamics and their associated driving factors represent highly complex systems of flows that are subject to non-linearities, sensitivities, and uncertainties across spatial and temporal scales. This project, led by the UK Centre for Ecology & Hydrology and Lancaster University, is developing a novel explainable AI framework that learns the complex spatial and temporal relationships between social, economic and environmental driving factors and historic agricultural land use change.

The framework is designed to be transparent, data-driven and spatially-explicit by probabilistically modelling land use dynamics. This approach will allow us to infer important characteristics of land use change using machine learning and parameter optimisation techniques.

This exploratory study will demonstrate proof-of-concept for selected regions in the UK and provide greater understanding of the state and dynamics of agricultural land use systems and how they can be influenced by policy and management decisions.

Principle Investigator Paula Harrison introduces the project in this short video
UK Centre for Ecology and Hydrology web page
Project contacts:
PI Prof Paula Harrison https://www.ceh.ac.uk/staff/paula-harrison
PI Dr Christopher Nemeth https://www.lancaster.ac.uk/maths/people/christopher-nemeth
Co-I Prof Peter Atkinson https://www.lancaster.ac.uk/people-profiles/peter-atkinson
Co -I Dr Ce Zhang https://www.lancaster.ac.uk/lec/about-us/people/ce-zhang
RA Dr Hongyan Chen https://www.lancaster.ac.uk/sci-tech/about-us/people/hongyan-chen
Link to the project page on the UKRI Gateway to Research https://gtr.ukri.org/projects?ref=NE%2FT003952%2F1