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 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 being designed to be transparent, data-driven and spatially-explicit by using probabilistic inference and explicit “if-then” rules. The explainable AI-rules will be characterised and refined through the framework using machine learning and parameter optimisation. 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.

For further information:

UKRI/NERC Strategic Priority Fund Landscape Decisions Programme

Associated members

Paula Harrison

UKCEH - Soils & Land Use

Paula is a Co-Director of CEEDS, Professor of Land and Water Modelling and Principal Natural Capital Scientist at the UK Centre for Ecology &…

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Ce Zhang

Member (Staff)
LU - Lancaster Environment Centre

Ce is a Lecturer in Geospatial Data Science based in CEEDS. He has extensive expertise in Artificial Intelligence, Machine Learning and Deep Learning…

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Christopher Nemeth

Member (Staff)
LU - School of Mathematics and Statistics

Chris Nemeth is a Lecturer in Statistical Learning in the Department of Mathematics and Statistics at Lancaster University. His research is in the…

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