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https://github.com/prgrmcode/tr-earthquake-predictor

Turkey Earthquake Prediction 🌍📊: Unleashing AI/ML powers in Python for seismic forecasts. #MachineLearning #Python #DataScience 🤖
https://github.com/prgrmcode/tr-earthquake-predictor

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

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Turkey Earthquake Prediction 🌍📊: Unleashing AI/ML powers in Python for seismic forecasts. #MachineLearning #Python #DataScience 🤖

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README 

        
# TR Earthquake AI Project



![Earthquake Image](earthquake.jpg)



## Table of Contents



- [Introduction](#introduction)

- [Project Overview](#project-overview)

- [Installation](#installation)

  - [Using Conda](#using-conda)

  - [Using Pip](#using-pip)

- [Usage](#usage)

- [Data](#data)

- [Features](#features)

- [Model](#model)

- [Results](#results)

- [Contributing](#contributing)

- [License](#license)

- [Acknowledgments](#acknowledgments)



## Introduction



Welcome 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.



## Project Overview



In this project, we aim to:



- Predict earthquake magnitudes and locations.

- Analyze seismic data to identify patterns and trends.

- Provide valuable insights for earthquake preparedness and mitigation.



## Installation



### Using Conda



1. Clone the repository:



   ```bash

   git clone https://github.com/prgrmcode/tr-earthquake-predictor.git

   ```



2. Navigate to the project directory:



   ```bash

   cd tr-earthquake-predictor

   ```



3. Create a Conda environment:



   ```bash

   conda env create -f environment.yml

   ```



4. Activate the Conda environment:



   ```bash

   conda activate earthquake-ai

   ```



### Using Pip



1. Clone the repository:



   ```bash

   git clone https://github.com/yourusername/earthquake-ai-project.git

   ```



2. Navigate to the project directory:



   ```bash

   cd earthquake-ai-project

   ```



3. Create a Python virtual environment (optional but recommended):



   ```bash

   python -m venv venv

   ```



4. Activate the virtual environment:



   - On Windows:



     ```bash

     venv\Scripts\activate

     ```



   - On macOS and Linux:



     ```bash

     source venv/bin/activate

     ```



5. Install project dependencies:



   ```bash

   pip install -r requirements.txt

   ```



## Usage



To use the Earthquake AI project, follow these steps:



1. **Data Preparation**: Prepare your earthquake data in the required format. You can use the provided dataset or integrate your data.



2. **Data Preprocessing**: Clean and preprocess the data using the provided sections on Jupyter notebook.



3. **Model Training**: Train the machine learning models using the preprocessed data. You can use the provided scripts.



4. **Predictions**: Use the trained models to make earthquake predictions.



5. **Data Visualization**: Visualize earthquake patterns, trends, and predictions using the provided visualization tools.



## Data



The 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.



Please unzip the dataset and place it in the root directory.



## Features



- **DATE\_**: The date of the earthquake event.

- **LOCATION\_**: The location of the earthquake.

- **LAT**: The latitude coordinate of the earthquake.

- **LNG**: The longitude coordinate of the earthquake.

- **MAG**: The magnitude of the earthquake.

- **DEPTH**: The depth at which the earthquake occurred.

- **RECORDDATE**: The date at which the earthquake recorded to dataset.



## Model



We use 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).



## Results



Our model achieved impressive results in earthquake prediction. We provide details of our model's performance 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.



You can find the map of predicted MAG values:



- [Map of Predicted MAG values](https://prgrmcode.github.io/tr-earthquake-predictor/)










![Actual vs Predicted test data](./results_best_model/actual_predicted_test_data.png)



![Actual vs Predicted mag time](./results_best_model/actual_vs_predicted_mag_time.png)



![Histogram Prediction Errors](./results_best_model/histogram_prediction_errors.png)



![Mag over Time line](./results_best_model/MAG_over_time_line.png)



![Predicted MAG scatter plot](./results_best_model/scatter_plot_Predicted_MAG_values_at_locations.png)



## Contributing



Contributions to this project are welcome! You can contribute by:



- Reporting issues or bugs.

- Adding new features or enhancements.

- Improving documentation.

- Providing insights and suggestions.



Please follow our [contributing guidelines](CONTRIBUTING.md) for more details.



## License



This project is licensed under the [Apache-2.0 license](LICENSE).



## Acknowledgments



We 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



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