Timeline:2024/03-2026/03
Keywords: Climate Change, Reinforcement Learning, Wellbeing, Transport Simulation
Team participants: Miguel Costa, Francisco C. Pereira
Lead Organization: DTU – Technical University of Denmark

Description

As climate change alters the physical, built, and social environment that humans depend on for resources, tools to aid climate change adaptation planning are increasingly urgent. In response, MA’AT proposes to combine machine learning with health and environmental modelling to develop a fully functional prototype for a multi-modular AI framework to aid climate change adaptation planning under deep climate uncertainty.

Focusing on climate adaptation policies for rain events within the Copenhagen capital region as a case study, the MA’AT Proof-of-Concept will learn an optimal policy that maximises delayed rewards in the form of societal wellbeing, within an agent-based modelling (ABM) environment.

Using the shared socioeconomic pathways (SSPs) as the basis for probabilistic priors for projecting different exogenous variables over time, this 2-year project will demonstrate a fully integrated probabilistic modelling framework that captures the dynamic interactions between climate change-induced flooding and other stressors due to heavy rain, future adaptation plans, and human wellbeing.

Partners

• DTU – Technical University of Denmark

Sponsor

This work was supported by a research grant (VIL57387) from VILLUM FONDEN.