Climate change intensifies hydro-geological hazards by altering precipitation and temperature patterns, affecting soil moisture, vegetation, groundwater, and surface runoff. These changes generate spatially and temporally variable triggers for floods, landslides, and droughts, challenging traditional methods based on historical records. Artificial intelligence (AI) offers a promising pathway by integrating multi-source observations with physics-informed learning to capture complex processes and incorporating future climate scenarios to enhance community resilience. This session explores AI integration for hydro-geological hazards under a climate-driven context, focusing on modeling, evaluation, and decision support. Key questions include: How can AI models account for complex physical processes and dynamically update triggering thresholds? How can multi-timescale climate variability and CMIP6 scenarios be embedded while preserving physical consistency? How can predictions remain robust under nonstationarity and inform early warning and climate-resilient planning?
We invite contributions addressing these challenges, with interest in AI, climate scenarios, and multi-scale process coupling. Topics include: 1) AI for hydro-geological hazards:
• Prediction and early warning of floods, landslides, and droughts using machine/deep learning for susceptibility mapping, monitoring, and real-time alerts.
• AI-driven coupled hazard modeling integrating rainfall, surface water, groundwater, and geological processes using multi-source data.
• Remote sensing and big data applications for hazard detection, evolution tracking, and mapping from satellite, UAV, or radar data.
• Assessing impacts of climate variability and extreme events on hazard occurrence.
• AI methods integrating CMIP6 scenarios with bias correction and downscaling for training and inference.
• Modeling physical processes, e.g., hydrological interactions among atmosphere, vegetation, and soil.
• Explainable AI and decision support systems for transparent hazard management, urban planning, and engineering measures.
2) Evaluation and decision support under climate change:
• AI-driven or GIS-based decision support platforms for adaptive management, policy-making, and disaster risk reduction.
• Assessing socio-economic vulnerability, resilience, and adaptation trade-offs under climate change.
• Evaluating nature-based and sustainable solutions as strategies for climate-resilient planning.