TY - JOUR
AU1 - Ridzwan, Nurafiqah Syahirah Md
AU2 - Yusoff, Siti Harwani Md.
AB - For decades, earthquake prediction has been the focus of research using various methods and techniques. It is difficult to predict the size and location of the next earthquake after one has occurred. However, machine learning (ML)-based approaches and methods have shown promising results in earthquake prediction over the past few years. Thus, we compiled 31 studies on earthquake prediction using ML algorithms published from 2017 to 2021, with the aim of providing a comprehensive review of previous research. This study covered different geographical regions globally. Most of the models analysed in this study are keen on predicting the earthquake magnitude, trend and occurrence. A comparison of different types of seismic indicators and the performance of the algorithms were summarized to identify the best seismic indicators with a high-performance ML algorithm. Towards this end, we have discussed the highest performance of the ML algorithm for earthquake magnitude prediction and suggested a potential algorithm for future studies.
TI - Machine learning for earthquake prediction: a review (2017–2021)
JF - Earth Science Informatics
DO - 10.1007/s12145-023-00991-z
DA - 2023-06-01
UR - https://www.deepdyve.com/lp/springer-journals/machine-learning-for-earthquake-prediction-a-review-2017-2021-wBJHv5uCX2
SP - 1133
EP - 1149
VL - 16
IS - 2
DP - DeepDyve
ER -