diff --git a/.history/README_20250328221727.md b/.history/README_20250328221727.md new file mode 100644 index 0000000000000000000000000000000000000000..96a095ab61658eb94550df4246dc321be7ebba62 --- /dev/null +++ b/.history/README_20250328221727.md @@ -0,0 +1,3 @@ +## TOAR-classifier v2: A data-driven classification tool for global air quality stations + +<img src="./figures/toar_classifier_v2.png" alt="My image" with="100"> diff --git a/.history/README_20250328221948.md b/.history/README_20250328221948.md new file mode 100644 index 0000000000000000000000000000000000000000..00262eec684fc0d9b12d86255893f2594038f431 --- /dev/null +++ b/.history/README_20250328221948.md @@ -0,0 +1,3 @@ +## 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"> diff --git a/.history/README_20250328221958.md b/.history/README_20250328221958.md new file mode 100644 index 0000000000000000000000000000000000000000..00262eec684fc0d9b12d86255893f2594038f431 --- /dev/null +++ b/.history/README_20250328221958.md @@ -0,0 +1,3 @@ +## 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"> diff --git a/.history/README_20250328222004.md b/.history/README_20250328222004.md new file mode 100644 index 0000000000000000000000000000000000000000..e5ed8e9d0ee98f0a1f3f6b74fa0d48baad2e23bb --- /dev/null +++ b/.history/README_20250328222004.md @@ -0,0 +1,4 @@ +## 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"> + diff --git a/.history/README_20250328222021.md b/.history/README_20250328222021.md new file mode 100644 index 0000000000000000000000000000000000000000..10ccc5df0f4b1f753566ab8e8203c1d830e4a0f3 --- /dev/null +++ b/.history/README_20250328222021.md @@ -0,0 +1,4 @@ +## 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> + diff --git a/README.md b/README.md index ae43e667a936bfa45637a7a9d24d33c028317f72..10ccc5df0f4b1f753566ab8e8203c1d830e4a0f3 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,4 @@ ## TOAR-classifier v2: A data-driven classification tool for global air quality stations -<img src="./figures/toar_classifier_v2.png" alt="My image" with="100"> +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> +