Earthquake prediction technique: a comparative study

Abbas H. Hassin Alasadi, Kadhim Mahdi Hashim


Earthquakes are one of the most dangerous natural disasters facing humans because of their occurrence without warning and their impact on their lives and property. In addition, predicting seismic movement is one of the main research topics in seismic disaster prevention. In geological studies, scientists can predict and know the locations of earthquakes in the long term. Therefore, about 80% of the major global earthquakes lie along the Pacific Ring belt, known as the Ring of Fire. Machine learning methods have also been used for short-term earthquake prediction, and studies have applied the random forest method to determine the factors that precede earthquakes. The machine learning method was based on various decision trees, each of which predicted the time to the nearest oscillation. The third group of scientists used the hybrid prediction method, which combines machine learning and geological studies. This research deals with a review of most of the geological studies and machine learning techniques applied to earthquake data sets, which showed a total lack of prediction of potential earthquakes through one approach, so studies designed by geologists were combined with machine learning.


Earthquakes; Geology science; Hybrid; Machine learning; Prediction

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats