TR Earthquake AI Project

Table of Contents

• Introduction
• Project Overview
• Installation
  • Using Conda
  • Using Pip
• Usage
• Data
• Features
• Model
• Results
• Contributing
• License
• Acknowledgments

Introduction

Welcome to the TR Earthquake AI project! This project utilizes machine learning and artificial intelligence techniques to predict and analyze earthquakes using earthquake data from Turkey.

Project Overview

In this project, I 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:

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

2. Navigate to the project directory:

   cd tr-earthquake-predictor

3. Create a Conda environment:

   conda env create -f environment.yml

4. Activate the Conda environment:

   conda activate earthquake-ai

Using Pip

1. Clone the repository:

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

2. Navigate to the project directory:

   cd earthquake-ai-project

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

   python -m venv venv

4. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

5. Install project dependencies:

   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

I 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.

Results

The 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.

You can find the map of predicted MAG values:

• Map of Predicted MAG values

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 contributing guidelines for more details.

License

This project is licensed under the Apache-2.0 license.

Acknowledgments

I 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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