Open AccessAnalysis of Seismic data using Machine Learning Algorithms
Author(s) -
Aravabhumi Sruthi,
R. Bhargavi,
Vineesha Reddy Gospati
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1070/1/012042
Subject(s) - random forest , computer science , decision tree , algorithm , earthquake prediction , machine learning , data mining , k nearest neighbors algorithm , artificial intelligence , statistical classification , seismology , geology
Earthquakes result in a gigantic loss of lives and properties to people because of its powerful, devastating and deep action. Over the years, a lot of research is going on to forecast the likelihood of occurrence of an earthquake to minimize the loss. In this study, a data mining technique i.e., classification analysis has been applied to estimate the most accurate earthquake model. Previous seismic data were collected and classified by applying k-NN (k-nearest neighbors algorithm) and Random forest algorithms. k-NN is a supervised machine learning algorithm used for bigger datasets (generally for statistical estimation) to determine the accuracy of the model. Random forest algorithm is also a supervised algorithm which is used for both classification and regression. By using this algorithm, multiple decision trees can be created over the datasets as well as predicting and offering a solution. Analysis and visualization of the data has been done and subsequently a comparative analysis of these two algorithms were done and tested to obtain the efficiency in predicting the accuracy of the earthquake model in terms of earthquake magnitude and depth