<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10013721" mdate="2024-07-11 00:00:00"> <author>Viacheslav Shkuratskyy and Aminu Bello Usman and Michael O鈥橠ea and Mujeeb Ur Rehman and Saifur Rahman Sabuj</author> <title>A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity</title> <pages>380 - 387</pages> <year>2024</year> <volume>18</volume> <number>7</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/10013721</ee> <url>https://publications.waset.org/vol/211</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>This paper examines relationships between solar activity and earthquakes, it applied machine learning techniques Knearest neighbour, support vector regression, random forest regression, and long shortterm memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity, and earthquake frequency distribution by magnitude and depth. The findings showed that the long shortterm memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to effect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.</abstract> <index>Open Science Index 211, 2024</index> </article>