Deep-learning model forecasts toxic plume movement in urban environments within minutes

Deep-learning model forecasts toxic plume movement in urban environments within minutes

In 2023, a train carrying hazardous materials derailed in East Palestine, Ohio. In 2025, a series of destructive wildfires ravaged Los Angeles. In both cases, a toxic plume—a cloud of harmful airborne materials that disperse over time and space due to wind and turbulence—was released.

Deep Learning Model Predicts How Toxic Plumes Move Through Cities

Researchers from Lawrence Livermore National Laboratory describe a deep learning model capable of predicting how toxic plumes move through cities in real time. The model provides fast and reliable predictions of plume travel that can facilitate evacuation planning and early-warning systems.

LLNL: Deep Learning Model Predicts Toxic Plume Movement

In a study published in PNAS Nexus, researchers from Lawrence Livermore National Laboratory (LLNL) described a new deep learning model, called ST-GasNet, capable of predicting toxic plume behavior in just a few minutes. The post LLNL: Deep Learning Model Predicts Toxic Plume Movement appeared first on Inside HPC & AI News | High-Performance Computing & Artificial Intelligence.