Climate hazards can cause major disasters when they occur simultaneously as compound hazards. To understand the distribution of climate risk and inform adaptation policies, scientists need to simulate a large number of physically realistic and spatially coherent events. Current methods are limited by computational constraints and the probabilistic spatial distribution of compound events is not given sufficient attention. The bottleneck in current approaches lies in modelling the dependence structure between variables, as inference on parametric models suffers from the curse of dimensionality. Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings. We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal, combining this with traditional extreme value theory for controlled extrapolation of the tails. Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events, which can inform climate risk assessments for climate adaptation and disaster preparedness. The method developed is flexible and transferable to other multivariate and spatial climate datasets.
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
Johns Hopkins University（约翰斯·霍普金斯大学）；Data Science and AI Institute, Johns Hopkins University（约翰斯·霍普金斯大学数据科学与人工智能学院）
FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India（图形可视化计算实验室，国际信息科技学院班加罗尔，卡纳塔克邦560100，印度）
Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling
National University of Singapore（新加坡国立大学）；Southern University of Science and Technology（南方科技大学）；Eastern Institute of Technology（东方理工高等研究院）；Stanford University（斯坦福大学）
Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression
INRAE（法国国家农业食品与环境研究院）；Aix-Marseille Université（艾克斯-马赛大学）；Institut de Mécanique des Fluides de Toulouse (IMFT)（图卢兹流体力学研究所）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
Multimodal Deep Survival Analysis for Sinkhole Susceptibility
Huron High School（休伦高中）；University of Florida（佛罗里达大学）；Industrial and Systems Engineering（工业与系统工程系）；Forest, Fisheries, and Geomatics Sciences（森林、渔业与地理信息科学系）
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
Spatial Heterogeneity in Climate Risk and Human Flourishing: An Exploration with Generative AI
Institute for Quantitative Social Science, Harvard University（哈佛大学定量社会科学研究所）；Stockholm University（斯德哥尔摩大学）；Department of Global Political Studies, Malmö University（马尔默大学全球政治学系）；Center for Geographic Analysis, Harvard University（哈佛大学地理分析中心）
