Adam Stapleton

Combining data from agricultural, economic, meteorological, geological and demographic sources with satellite imagery to understand land-use changes over time and identify Pareto optimal conditions for future land use satisfying multiple criteria including economic output, finance, resource utilisation, supply and demand fluctuation due to population and demographic changes and climate change targets. A variety of methods will be explored in order to examine trade-offs in algorithm performance and interpretability. Reinforcement learning, Evolutionary, Classification Tree (combined with Monte Carlo Search methods) and Markov Decision Process based algorithms all provide themselves as candidates with varying degrees of success in other applications. The goal is to provide a set of Pareto-optimal trade-off solutions that would allow decision makes to compare different balances of conflicting objectives.