{"id":18655981,"url":"https://github.com/prgrmcode/tr-earthquake-predictor","last_synced_at":"2026-01-28T14:31:45.934Z","repository":{"id":194202263,"uuid":"690076698","full_name":"prgrmcode/tr-earthquake-predictor","owner":"prgrmcode","description":"Turkey Earthquake Prediction 🌍📊: Unleashing AI/ML powers in Python for seismic forecasts. #MachineLearning #Python #DataScience 🤖","archived":false,"fork":false,"pushed_at":"2024-11-23T00:38:21.000Z","size":60499,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-12T14:15:18.526Z","etag":null,"topics":["data-science","dataset","deep-learning","ensemble-learning","hyperparameter-tuning","kaggle","machine-learning","neural-networks","preprocessing","python","random-forest","random-forest-regression","svr","tensorflow","xgboost","xgboost-model"],"latest_commit_sha":null,"homepage":"https://colab.research.google.com/github/prgrmcode/tr-earthquake-predictor/blob/main/ProjectAIEarthquake.ipynb","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/prgrmcode.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-09-11T13:39:17.000Z","updated_at":"2024-11-23T00:38:26.000Z","dependencies_parsed_at":"2023-10-11T18:26:35.390Z","dependency_job_id":null,"html_url":"https://github.com/prgrmcode/tr-earthquake-predictor","commit_stats":{"total_commits":15,"total_committers":2,"mean_commits":7.5,"dds":0.4,"last_synced_commit":"c8685fa95960b18dd21b55cfcb12b943f1b70157"},"previous_names":["prgrmcode/tr-earthquake-predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prgrmcode%2Ftr-earthquake-predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prgrmcode%2Ftr-earthquake-predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prgrmcode%2Ftr-earthquake-predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prgrmcode%2Ftr-earthquake-predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/prgrmcode","download_url":"https://codeload.github.com/prgrmcode/tr-earthquake-predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253754262,"owners_count":21958843,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","dataset","deep-learning","ensemble-learning","hyperparameter-tuning","kaggle","machine-learning","neural-networks","preprocessing","python","random-forest","random-forest-regression","svr","tensorflow","xgboost","xgboost-model"],"created_at":"2024-11-07T07:21:08.881Z","updated_at":"2026-01-28T14:31:45.927Z","avatar_url":"https://github.com/prgrmcode.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TR Earthquake AI Project\n\n![Earthquake Image](earthquake.jpg)\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Project Overview](#project-overview)\n- [Installation](#installation)\n  - [Using Conda](#using-conda)\n  - [Using Pip](#using-pip)\n- [Usage](#usage)\n- [Data](#data)\n- [Features](#features)\n- [Model](#model)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## Introduction\n\nWelcome to the [TR Earthquake AI project!](ProjectAIEarthquake.ipynb) This project utilizes machine learning and artificial intelligence techniques to predict and analyze earthquakes using earthquake data from Turkey.\n\n## Project Overview\n\nIn this project, I aim to:\n\n- Predict earthquake magnitudes and locations.\n- Analyze seismic data to identify patterns and trends.\n- Provide valuable insights for earthquake preparedness and mitigation.\n\n## Installation\n\n### Using Conda\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/prgrmcode/tr-earthquake-predictor.git\n   ```\n\n2. Navigate to the project directory:\n\n   ```bash\n   cd tr-earthquake-predictor\n   ```\n\n3. Create a Conda environment:\n\n   ```bash\n   conda env create -f environment.yml\n   ```\n\n4. Activate the Conda environment:\n\n   ```bash\n   conda activate earthquake-ai\n   ```\n\n### Using Pip\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/yourusername/earthquake-ai-project.git\n   ```\n\n2. Navigate to the project directory:\n\n   ```bash\n   cd earthquake-ai-project\n   ```\n\n3. Create a Python virtual environment (optional but recommended):\n\n   ```bash\n   python -m venv venv\n   ```\n\n4. Activate the virtual environment:\n\n   - On Windows:\n\n     ```bash\n     venv\\Scripts\\activate\n     ```\n\n   - On macOS and Linux:\n\n     ```bash\n     source venv/bin/activate\n     ```\n\n5. Install project dependencies:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\nTo use the Earthquake AI project, follow these steps:\n\n1. **Data Preparation**: Prepare your earthquake data in the required format. You can use the provided dataset or integrate your data.\n\n2. **Data Preprocessing**: Clean and preprocess the data using the provided sections on Jupyter notebook.\n\n3. **Model Training**: Train the machine learning models using the preprocessed data. You can use the provided scripts.\n\n4. **Predictions**: Use the trained models to make earthquake predictions.\n\n5. **Data Visualization**: Visualize earthquake patterns, trends, and predictions using the provided visualization tools.\n\n## Data\n\nThe project uses earthquake data from Turkey, including features like date, location, latitude, longitude, magnitude, depth, and more. The dataset is available in the `data` directory and in dataset location: https://www.kaggle.com/datasets/serhatk/turkey-20-years-earthquakes-csv.\n\nPlease unzip the dataset and place it in the root directory.\n\n## Features\n\n- **DATE\\_**: The date of the earthquake event.\n- **LOCATION\\_**: The location of the earthquake.\n- **LAT**: The latitude coordinate of the earthquake.\n- **LNG**: The longitude coordinate of the earthquake.\n- **MAG**: The magnitude of the earthquake.\n- **DEPTH**: The depth at which the earthquake occurred.\n- **RECORDDATE**: The date at which the earthquake recorded to dataset.\n\n## Model\n\nI developed machine learning models to predict earthquake magnitudes. The models are trained on historical earthquake data and is available in the `'VI. Experiment with Multiple Regression Models'` section of the [Jupyter notebook](ProjectAIEarthquake.ipynb).\n\n## Results\n\nThe project achieved impressive results in earthquake prediction. The details of the model's performance are provided in the `results_best_model` directory and `'X. Using best model XGBRegressor with the best Hyperparameters to make predictions on new data'` section of the 'ProjectAIEarthquake.ipynb' Jupyter Notebook.\n\nYou can find the map of predicted MAG values:\n\n- [Map of Predicted MAG values](https://prgrmcode.github.io/tr-earthquake-predictor/)\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n![Actual vs Predicted test data](./results_best_model/actual_predicted_test_data.png)\n\n![Actual vs Predicted mag time](./results_best_model/actual_vs_predicted_mag_time.png)\n\n![Histogram Prediction Errors](./results_best_model/histogram_prediction_errors.png)\n\n![Mag over Time line](./results_best_model/MAG_over_time_line.png)\n\n![Predicted MAG scatter plot](./results_best_model/scatter_plot_Predicted_MAG_values_at_locations.png)\n\n\n\n## Contributing\n\nContributions to this project are welcome! You can contribute by:\n\n- Reporting issues or bugs.\n- Adding new features or enhancements.\n- Improving documentation.\n- Providing insights and suggestions.\n\nPlease follow [contributing guidelines](CONTRIBUTING.md) for more details.\n\n## License\n\nThis project is licensed under the [Apache-2.0 license](LICENSE).\n\n## Acknowledgments\n\nI would like to thank the open-source community for their contributions and the earthquake data providers AFAD agency for making their data available. Also thanks to the account holders of kaggle dataset: https://www.kaggle.com/datasets/serhatk/turkey-20-years-earthquakes-csv\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprgrmcode%2Ftr-earthquake-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprgrmcode%2Ftr-earthquake-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprgrmcode%2Ftr-earthquake-predictor/lists"}