Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Machine learning (ML) has evolved rapidly over recent years with the promise to
substantially alter and enhance the role of data science in a variety of disciplines. Compared …

A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble
Machine Learning) has been developed in this paper to predict the peak ground …

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a
subdivision of artificial intelligence based on the biological learning process. The ML …

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling
nonlinear systems. The proposed strategy is based on incorporating the individual effect of …

Realistically predicting earthquake is critical for seismic risk assessment, prevention and
safe design of major structures. Due to the complex nature of seismic events, it is …

In this research, new models are developed to estimate the three principal time-domain
parameters of seismic ground motion. A novel deep learning (DL) approach coupled with …

Rapid and accurate prediction of peak ground acceleration (PGA) is an important basis for
determining seismic damage through on-site earthquake early warning (EEW). The current …

In the present study, an efficient bagging ensemble model based on two well-known
decision tree algorithms, namely, M5′ and Classification and Regression Trees (CART) is …

Peak ground acceleration (PGA) has still been considered one of the important factors that
plays significant role on the earthquake-induced damage of structures. Thus, prediction of …