Add image

.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 = {}
+}
+

README.md

@@ -16,7 +16,7 @@ This study develops a machine learning approach to classify 23,974 air quality m
 2. Install Jupyter Notebook:
    - `pip install notebook` (for Jupyter Notebook) or
    - `pip install jupyterlab` (for JupyterLab).
-3. clone the project by running the following
+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`
@@ -26,7 +26,6 @@ This study develops a machine learning approach to classify 23,974 air quality m
    - `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.