Earthquake Prediction using Machine Learning

This project aims to predict earthquake magnitudes and occurrences using machine learning models.

Project Structure

earthquake-prediction-ml/
├── data/
│   └── earthquake_data.csv
├── src/
│   └── main.py
├── README.md
├── LICENSE
└── requirements.txt

Installation

1. Clone the repository:

git clone https://github.com/oaslananka/EarthquakePredictionML.git
cd EarthquakePredictionML

2. Install the dependencies:

pip install -r requirements.txt

Usage

1. Run the main script:

python src/main.py

Data Source

The earthquake data is fetched from the USGS (United States Geological Survey) API. The data includes information about earthquake magnitudes, locations, depths, and times.

Algorithms and Methods

This project uses several machine learning algorithms for earthquake prediction, including:

• Random Forest Regressor
• Gradient Boosting Regressor
• XGBoost Regressor

The models are trained using features engineered from the raw earthquake data, such as statistical measures, zero-crossings, peak counts, FFT values, and the Hilbert transform's amplitude envelope.

Example Project

This project serves as an example of using machine learning techniques for predicting natural events. It demonstrates data fetching, preprocessing, feature engineering, model training, and prediction.

License

This project is licensed under the MIT License. See the LICENSE file for details.