Primary Convener:
Xin Wei, University of Michigan Ann Arbor

Convener:
Chuxuan Li, University of California Los Angeles
Jingxiao Liu, Massachusetts Institute of Technology
Bingxu Luo, The University of Arizona

Early Career Convener:
Ann Sinclair, Northwestern University

Chair:
Xin Wei, University of Michigan Ann Arbor
Chuxuan Li, University of California Los Angeles
Bingxu Luo, The University of Arizona
Ann Sinclair, Northwestern University

To address the growing challenges of geohazards, we invite contributions that advance reliable and scalable machine learning/artificial intelligence (ML/AI) approaches for the detection, monitoring, and prediction of geohazards, such as earthquakes, tsunamis, volcanoes, landslides, and surface subsidence. We especially welcome works that (1) integrate multi-scale sensing technologies (e.g., remote sensing and distributed fiber-optic sensing) with ML/AI to support both pre- and post-event assessment of geohazards as well as their cascading impacts in diverse environmental settings, such as urbanized, remote, post-disturbance, and cold landscapes; (2) develop interpretable and knowledge-guided (e.g., physics-informed) ML/AI to reveal the driving factors and physical mechanisms; (3) evaluate and improve model robustness in extreme and data-scarce scenarios through cross-region and cross-scenario model transfer, uncertainty quantification, and real-time data fusion; and (4) develop open-source, multi-scale geohazard datasets to support the training and testing of foundation models toward reliable, scalable, real-world AI deployment.

Index Terms
4307 Methods
4315 Monitoring, forecasting, prediction
4333 Disaster risk analysis and assessment
4337 Remote sensing and disasters

Cross-Listed:
NS - Near Surface Geophysics
H - Hydrology
EP - Earth and Planetary Surface Processes
GC - Global Environmental Change

Neighborhoods:
1. Science Nexus