Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
NH10.7 | Unravelling the complexity of risk: how artificial intelligence and big data can provide insights into climate risk dynamics in the era of interdependencies.
EDI
Unravelling the complexity of risk: how artificial intelligence and big data can provide insights into climate risk dynamics in the era of interdependencies.
Convener: Silvia Torresan | Co-conveners: Saman Ghaffarian, Massimiliano Pittore, Benjamin Trump
The interconnections among socio-economic, environmental and climate risks are presenting new challenges to our society. The complexity and occurrence of compound events, such as droughts and heatwaves, heavy precipitation and wind extremes, are increasing, as well as the potential for cascading effects to propagate across multiple sectors ( e.g. finance, ecosystems, agriculture, critical infrastructure, health and social networks). To effectively analyze risks and resilience in this context, conceptual and methodological approaches must consider the impact of heightened uncertainties in both datasets and decision-making processes. Traditional approaches often focusing on risks from single hazards and within individual sectors, underestimate inter-sector and cascading effects, and are no longer sufficient for comprehensive analysis of climate change's impact on complex systems. Consequently, there is an urgent need for innovative solutions that can leverage the vast potential of artificial intelligence and machine learning to address these challenges.

Within this framework, harnessing the capabilities of new technologies such as artificial intelligence and machine learning, integrated with remote sensing and earth observation, digital twins, IoT, and data fusion, emerges as a critical undertaking. This endeavour holds the promise of transforming our understanding of multi-hazard and multi-sector risks, elevating the analysis of resilience to a new level.

This session will provide a platform to explore and discuss recent advancements, applications, and challenges of artificial intelligence (AI) in the context of multi-sector and multi-hazard climate related risk and resilience analytics. We invite papers that showcase advancements and applications of cutting-edge AI methods to model the complexity of multi sector compounding and cascading risks; to uncover shared vulnerabilities and common points of failure between different sectors; to predict and manage the resilience of socio-ecological systems to complex, High-Impact Low-Probability events (considering the trade-offs and tipping points for environmental and social risks); to assess future scenarios driven by global and regional drivers and to help formulating adaptive strategies.