Earthquake Prediction Using Machine Learning

Overview

This project leverages machine learning techniques to predict earthquake occurrences based on historical seismic data. The model is trained on structured geophysical datasets to analyze patterns and trends leading to seismic events.

Features

• Supervised Learning Models: Implements regression and classification algorithms for earthquake forecasting.
• Feature Engineering: Includes data preprocessing, feature selection, and handling missing values.
• Evaluation Metrics: Accuracy, Precision, Recall, RMSE, and F1-score are used for model assessment.

Dataset

The dataset consists of:

• Seismic activity records, including magnitude, depth, and location.
• Temporal patterns of earthquake occurrences.
• Geological and environmental factors contributing to seismic events.

Installation & Requirements

Ensure you have Python installed, then install the required dependencies:

pip install pandas numpy scikit-learn matplotlib seaborn

Usage

1. Clone the repository:

   git clone https://github.com/kilofrakh/Earthquake-Prediction.git

2. Navigate to the project directory:

   cd Earthquake-Prediction

3. Run the Jupyter Notebook or Python script to train and evaluate the model.

Model Performance

• Confusion Matrix for Classification Models
• Regression Metrics for Magnitude Prediction
• Feature Importance Analysis

Future Enhancements

• Integration with real-time seismic data for live predictions.
• Application of deep learning models for improved accuracy.
• Deployment as a web-based prediction tool.

Contributing

Feel free to fork the repository and submit pull requests with improvements.

License

This project is licensed under the MIT License.

Contact

For queries, contact:

• Abdelkareem Hossam
• Email: [email protected]
• GitHub: kilofrakh