From a13bad188ea8dde32c07ca21eab87b9c340bce81 Mon Sep 17 00:00:00 2001 From: Karim Mache <k.mache@fz-juelich.de> Date: Fri, 28 Mar 2025 22:36:42 +0100 Subject: [PATCH] Add image --- .history/README_20250328222519.md | 5 +++++ .history/README_20250328222601.md | 7 +++++++ .history/README_20250328223018.md | 7 +++++++ .history/README_20250328223037.md | 7 +++++++ .history/README_20250328223328.md | 9 +++++++++ .history/README_20250328223358.md | 9 +++++++++ .history/README_20250328223446.md | 10 ++++++++++ .history/README_20250328223627.md | 10 ++++++++++ README.md | 6 ++++++ 9 files changed, 70 insertions(+) create mode 100644 .history/README_20250328222519.md create mode 100644 .history/README_20250328222601.md create mode 100644 .history/README_20250328223018.md create mode 100644 .history/README_20250328223037.md create mode 100644 .history/README_20250328223328.md create mode 100644 .history/README_20250328223358.md create mode 100644 .history/README_20250328223446.md create mode 100644 .history/README_20250328223627.md diff --git a/.history/README_20250328222519.md b/.history/README_20250328222519.md new file mode 100644 index 0000000..303f6bd --- /dev/null +++ b/.history/README_20250328222519.md @@ -0,0 +1,5 @@ +## 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=100> + +### Run code \ No newline at end of file diff --git a/.history/README_20250328222601.md b/.history/README_20250328222601.md new file mode 100644 index 0000000..e9ab70c --- /dev/null +++ b/.history/README_20250328222601.md @@ -0,0 +1,7 @@ +## 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=100> + + + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223018.md b/.history/README_20250328223018.md new file mode 100644 index 0000000..ae41b64 --- /dev/null +++ b/.history/README_20250328223018.md @@ -0,0 +1,7 @@ +## 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=100> + +`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model. + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223037.md b/.history/README_20250328223037.md new file mode 100644 index 0000000..ae41b64 --- /dev/null +++ b/.history/README_20250328223037.md @@ -0,0 +1,7 @@ +## 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=100> + +`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model. + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223328.md b/.history/README_20250328223328.md new file mode 100644 index 0000000..8e04628 --- /dev/null +++ b/.history/README_20250328223328.md @@ -0,0 +1,9 @@ +## 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=100> + +`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model. +`TOAR-classifier_v2.ipynb` is the notebook contening the code +`requirements.txt` containts + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223358.md b/.history/README_20250328223358.md new file mode 100644 index 0000000..0c79a57 --- /dev/null +++ b/.history/README_20250328223358.md @@ -0,0 +1,9 @@ +## 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=100> + +`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model. +`TOAR-classifier_v2.ipynb` is the notebook contening the code +`requirements.txt` containts all neccessary packages. + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223446.md b/.history/README_20250328223446.md new file mode 100644 index 0000000..d561c59 --- /dev/null +++ b/.history/README_20250328223446.md @@ -0,0 +1,10 @@ +## 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=100> + +### Files +`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model. +`TOAR-classifier_v2.ipynb` is the notebook contening the code +`requirements.txt` containts all neccessary packages. + +### Run code \ No newline at end of file diff --git a/.history/README_20250328223627.md b/.history/README_20250328223627.md new file mode 100644 index 0000000..bc18d1e --- /dev/null +++ b/.history/README_20250328223627.md @@ -0,0 +1,10 @@ +## 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=100> + +### Files +`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model. +`TOAR-classifier_v2.ipynb` is the notebook contening the code +`requirements.txt` contains all neccessary packages. + +### Run code \ No newline at end of file diff --git a/README.md b/README.md index 10ccc5d..bc18d1e 100644 --- a/README.md +++ b/README.md @@ -2,3 +2,9 @@ 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=100> +### Files +`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model. +`TOAR-classifier_v2.ipynb` is the notebook contening the code +`requirements.txt` contains all neccessary packages. + +### Run code \ No newline at end of file -- GitLab