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🌍 Welcome to the Earthquake Prediction Analysis Project! 🚀 This project aims to predict earthquake magnitudes using LSTM neural networks and analyze seismic data. Explore, analyze, and forecast earthquakes with ease! 📈🔮
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Welcome to the Earthquake Prediction Analysis Project! This project aims to predict the magnitude of earthquakes using time series data and LSTM (Long Short-Term Memory) neural networks.
Earthquake prediction is a critical area of research aiming to mitigate the impact of seismic activities on society and infrastructure. This project utilizes machine learning techniques, particularly LSTM neural networks, to forecast the magnitude of earthquakes based on historical seismic data.
To install and run this project locally, follow these steps:
requirements.txt file using pip:
pip install -r requirements.txt
earthquake_prediction.py to train the LSTM model and make predictions.After installing the necessary dependencies and running the main script, you can use the trained LSTM model to make earthquake magnitude predictions. Additionally, you can customize the model architecture and hyperparameters to improve prediction accuracy.
The dataset utilized in this project comprises historical earthquake records, encompassing attributes such as timestamp, geographic coordinates, depth, and magnitude. Prior to employment, the data undergoes preprocessing and partitioning into training and testing subsets for model training and assessment.
Live earthquake data from the Kandilli Observatory and Earthquake Research Institute can be accessed through the following API endpoint:
curl -X 'GET' \
'https://api.orhanaydogdu.com.tr/deprem/kandilli/live' \
-H 'accept: application/json'
| Date & Time (UTC) | Location_tz | Depth (km) | Magnitude |
|---|---|---|---|
| 2024-03-19 21:36:40 | Europe/Istanbul | 7 | 2.8 |
| 2024-03-19 21:14:44 | Europe/Istanbul | 6.8 | 1.9 |
| 2024-03-19 19:48:26 | Europe/Istanbul | 12.5 | 1.7 |
| 2024-03-19 19:43:28 | Europe/Istanbul | 10.2 | 2.9 |
| 2024-03-19 19:40:16 | Europe/Istanbul | 5 | 2.4 |
The LSTM neural network architecture used in this project consists of multiple LSTM layers followed by dense layers for regression. The model takes sequential earthquake data as input and learns to predict the magnitude of future earthquakes.
The performance of the LSTM model is evaluated using the following metrics:
Additionally, the correlation coefficient between predicted and actual earthquake magnitudes is calculated to assess the model's performance.
Metric | Value |
|---|---|
| Mean Squared Error (MSE) | 1.9480 |
| Root Mean Squared Error (RMSE) | 1.3957 |
| Mean Absolute Error (MAE) | 1.3818 |
There are several opportunities for future improvement and expansion of this project:
If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to use, modify, and distribute this code for educational and research purposes. If you find this project helpful, consider giving it a star on GitHub!