What question did this study set out to answer?

This study aims to develop a machine learning framework for accurate earthquake prediction using historical seismic data.

April 25, 2026Open Access

The Earthquake Prediction Using Machine Learning Algorithms: A Data-Driven Framework Based on Supervised Learning Models

View Full Paper

Ask AI

Bookmark

This study aims to develop a machine learning framework for accurate earthquake prediction using historical seismic data.
Data acquisition and preprocessing of seismic records from the USGS
Class balancing and feature preparation to address class imbalance
Model development and evaluation with multiple supervised learning algorithms
Random Forest achieved the highest binary classification accuracy of 98.15%, AUC of 0.683
Neural Network achieved the highest recall of 0.611 for early warnings
Multi-class classification showed Random Forest with an accuracy of 87.78%, outperforming other models

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Randomized trial demonstrates effective earthquake prediction using machine learning models in seismology, indicating significant improvements for early warnings.

Cite This Study

Gamal et al. (2026) studied this question.

synapsesocial.com/papers/69ec5a2588ba6daa22dabbd6 https://doi.org/10.19139/soic-2310-5070-3417

View Full Paper

Ask AI

Bookmark

Implication

Randomized trial demonstrates effective earthquake prediction using machine learning models in seismology, indicating significant improvements for early warnings.

The Earthquake Prediction Using Machine Learning Algorithms: A Data-Driven Framework Based on Supervised Learning Models

Key Points

Discussion

Implication

Cite This Study

The Earthquake Prediction Using Machine Learning Algorithms: A Data-Driven Framework Based on Supervised Learning Models

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider