.history/README_20250329005754.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+   -`python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329005823.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329005842.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329005946.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329010042.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+3. clone the project by running the following command
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329010106.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+3. clone the project by running the following command
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329013807.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+3. clone the project by running the following command
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+7. Install required package
+   - open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+   - Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+8. Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/requirements_20250328224110.txt

+fancyimpute
\ No newline at end of file

.history/requirements_20250328224142.txt

+fancyimpute
+catboost
+pandas
+lightgbm
\ No newline at end of file

.history/requirements_20250328224332.txt

+fancyimpute
+catboost
+pandas
+lightgbm
+imblearn
+seaborn
\ No newline at end of file

.history/requirements_20250328224340.txt

+fancyimpute
+catboost
+pandas
+lightgbm
+imblearn
+seaborn
\ No newline at end of file

LICENSE

+The MIT License (MIT)
+
+Copyright (c) 2025 Ramiyou Karim Mache
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+

README.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+3. clone the project by running the following command
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+4. Change directory to ml_toar_station_classification
+   - `cd ml_toar_station_classification`
+5. Creat virtual environment 
+   - `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+6. Activate the created venv 
+   - `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+7. Install required package
+   - open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+   - Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+8. Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

catboost_info/learn_error.tsv

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