This is a deep learning model for predicting earthquake occurrences based on real-time data. The model utilizes various data sources, including seismic activity, weather patterns, and geological data, to provide accurate and timely predictions. The model is built using TensorFlow and Keras, and uses a combination of convolutional and recurrent neural networks to analyze the data and detect patterns that indicate an earthquake is imminent. The model is trained on historical earthquake data, and is designed to continuously learn and adapt to new data in order to improve its accuracy over time.
The goal of this project is to provide an early warning system for earthquakes that can help to save lives and mitigate damage. By analyzing multiple data sources in real-time and predicting earthquake occurrences with a high degree of accuracy, this model has the potential to revolutionize the field of earthquake prediction and improve our ability to respond to these natural disasters.
To run this project, you'll need to have the following software installed:
- Python (3.7 or higher)
- TensorFlow (2.0 or higher)
- Keras (2.3 or higher)
- NumPy (1.17 or higher)
- Matplotlib (3.1 or higher)
To install the required packages, run the following command:
pip install -r requirements.txt
To train the model, run the following command:
python train.py
This will generate a trained model in the model directory.
To make predictions using the model, run the following command:
python predict.py
This will generate earthquake predictions based on real-time data.
If you'd like to contribute to this project, please follow these guidelines:
- Fork the repository and create a new branch.
- Make your changes and test them thoroughly.
- Submit a pull request with a detailed description of your changes.
This project is licensed under the MIT License - see the LICENSE.md file for details.