Disaster risk reduction and management demand looking beyond single hazards to the interacting, cascading, and compounding processes that shape impacts on people, infrastructure, and ecosystems. This session showcases advances in multi-(hazard) risk assessment from characterizing hazards, exposure, vulnerability, and capacity across sectors to operational tools that support anticipatory action and resilient recovery.
We welcome contributions that bridge science, engineering, data science, and social dimensions; quantify uncertainty; and turn analysis into policy and practice. We invite work that couples geophysical insight with emerging technologies: AI/ML, digital twins, remote sensing, impact-based forecasting, and decision-support platforms to reveal systemic risks and inform risk-aware planning under climate and socio-economic change. Case studies from Japan, Europe, and other regions are encouraged to leverage the community.
Potential topics include:
Methods for multi-(hazard) risk: integrated analysis of interactions, exposure dynamics, vulnerability, and capacity across scales; Impact-based multi-hazard forecasting and early warning (near-real-time use, thresholds design); Uncertainty analysis and climate/impact attribution for compound and concurrent extremes; AI/ML tools for multi-hazard, multi-sector, and systemic risk management; Novel technologies for data collection and generation, (e.g., LLM, Earth Observations); Digital twins and network/system models of cascading failures and infrastructure interdependencies; Decision-support tools and open-source platforms co-developed with stakeholders; usability, ethics, and governance; Risk communication, knowledge sharing, and capacity building, including inclusive, community-centered approaches; Transferability and scalability of innovations across regions, hazards, and sectors; best practices for uptake; Synergies and trade-offs among DRR measures across hazards; lessons from implementation and evaluation.

Disaster risk reduction and management demand looking beyond single hazards to the interacting, cascading, and compounding processes that shape impacts on people, infrastructure, and ecosystems. This session showcases advances in multi-(hazard) risk assessment from characterizing hazards, exposure, vulnerability, and capacity across sectors to operational tools that support anticipatory action and resilient recovery.
We welcome contributions that bridge science, engineering, data science, and social dimensions; quantify uncertainty; and turn analysis into policy and practice. We invite work that couples geophysical insight with emerging technologies: AI/ML, digital twins, remote sensing, impact-based forecasting, and decision-support platforms to reveal systemic risks and inform risk-aware planning under climate and socio-economic change. Case studies from Japan, Europe, and other regions are encouraged to leverage the community.
Potential topics include:
Methods for multi-(hazard) risk: integrated analysis of interactions, exposure dynamics, vulnerability, and capacity across scales; Impact-based multi-hazard forecasting and early warning (near-real-time use, thresholds design); Uncertainty analysis and climate/impact attribution for compound and concurrent extremes; AI/ML tools for multi-hazard, multi-sector, and systemic risk management; Novel technologies for data collection and generation, (e.g., LLM, Earth Observations); Digital twins and network/system models of cascading failures and infrastructure interdependencies; Decision-support tools and open-source platforms co-developed with stakeholders; usability, ethics, and governance; Risk communication, knowledge sharing, and capacity building, including inclusive, community-centered approaches; Transferability and scalability of innovations across regions, hazards, and sectors; best practices for uptake; Synergies and trade-offs among DRR measures across hazards; lessons from implementation and evaluation.