This paper is published in Volume-7, Issue-3, 2021
Area
Computers
Author
Madhumita Kulkarni, Chaitralee Mulay, Swarali Marathe, Prashant Itankar
Org/Univ
Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India
Pub. Date
29 May, 2021
Paper ID
V7I3-1563
Publisher
Keywords
Ad boost classifier (Random Forest Classifier and Decision Tree Classifier), XG Boost, Machine Learning, Earthquake Dataset

Citationsacebook

IEEE
Madhumita Kulkarni, Chaitralee Mulay, Swarali Marathe, Prashant Itankar. Earthquake Prediction using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Madhumita Kulkarni, Chaitralee Mulay, Swarali Marathe, Prashant Itankar (2021). Earthquake Prediction using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 7(3) www.IJARIIT.com.

MLA
Madhumita Kulkarni, Chaitralee Mulay, Swarali Marathe, Prashant Itankar. "Earthquake Prediction using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 7.3 (2021). www.IJARIIT.com.

Abstract

Earthquake forecasting is one of the most significant issues in Earth science because of its devastating consequences. Current earthquake forecasting scientific studies focus on three key points: when the disaster will occur, where it will occur and how big it will be. Scientists can predict where an earthquake will occur but it has been a major challenge to predict when it will occur and how powerful it will be. When the earthquake happens, we must fix this project. Specifically, you predict the time left before laboratory earthquakes occur from real-time seismic data that will have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure.