A Real-Time Seismic Intensity Prediction Framework Based on Machine Learning for Earthquake Early Warning

Predicting Seismic Intensity Using Machine Learning With Ancillary Data: Evidence From Destructive Earthquakes in China

Accurate and rapid assessment of seismic intensity is crucial for postearthquake emergency response. This is especially true during the initial “black‐box” period, when real‐time data are scarce. To address this challenge, we developed a machine learning‐based (ML) framework for predicting kilometer‐grid‐scale seismic...

Machine Learning–based Earthquake Probability Prediction for Istanbul Province

Earthquakes represent one of the most destructive natural hazards, causing severe social and economic losses, particularly in tectonically active regions. In this context, estimating the short-term probability of earthquake occurrence is of critical importance for disaster preparedness and risk mitigation. This study f...

A data-driven framework for accurate surface spectral acceleration prediction with integrated uncertainty control

An Advanced Multi-Model Fusion Approach for Earthquake Prediction with Uncertainty Estimation from Seismic Sensor Streams

The ability to predict when an earthquake will happen along with issuing an early warning is important in order to prevent problems from occurring due to seismic activity. The large amounts of non-linear and complex nature of seismic data usually leads to traditional machine learning models not having the ability to pr...

Machine Learning for the Spatiotemporal Prediction of Seismic Ground Motion Fields: A State‐of‐the‐Art Review

Spatiotemporal prediction of seismic ground motion represents a critical scientific challenge in earthquake engineering and seismology, with important implications for seismic design, rapid post‐earthquake damage assessment, and emergency response. Traditional methods, which primarily rely on physics‐based simulation...

The evolution of seismic intensity measures: From classical to machine learning-based seismic intensity measures for different structures—Data-driven systematic review