Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand
Satellite imagery is utilized in various fields, one of which is land use and land cover (LULC) analysis. This study aims to classify water bodies using machine learning models such as SVM, K-NN, RF, CART, and GNB. The data source is obtained from the Google Earth Engine (GEE) platform using Sentine...
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| Format: | Article |
| Language: | English |
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Universitas Sanata Dharma
2025-06-01
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| Series: | International Journal of Applied Sciences and Smart Technologies |
| Online Access: | https://e-journal.usd.ac.id/index.php/IJASST/article/view/11608 |
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| author | Ni Putu Nita Nathalia Gede Andra Rizqy Wijaya Kadek Yota Ernanda Aryanto Ni Putu Novita Puspa Dewi Putu Hendra Saputra Ni Putu Karisma Dewi Mellisa Damayanti Kadek Losinanda Prawira |
| author_facet | Ni Putu Nita Nathalia Gede Andra Rizqy Wijaya Kadek Yota Ernanda Aryanto Ni Putu Novita Puspa Dewi Putu Hendra Saputra Ni Putu Karisma Dewi Mellisa Damayanti Kadek Losinanda Prawira |
| author_sort | Ni Putu Nita Nathalia |
| collection | DOAJ |
| description | Satellite imagery is utilized in various fields, one of which is land use and land cover (LULC) analysis. This study aims to classify water bodies using machine learning models such as SVM, K-NN, RF, CART, and GNB. The data source is obtained from the Google Earth Engine (GEE) platform using Sentinel-2 Level-2A satellite imagery, with a dataset of 5,514 data points per year. The Pixel-Based approach is used as the main method for data extraction, while CRISP-DM is applied as a structured methodology for data management. The parameter indices used include the BSI, NDBI, MNDWI, NDVI and AWEIsh. The results of these calculations serve as dataset features for training algorithms in the model development and training process. Each model has its own parameters, making parameter selection crucial in the training process. Model evaluation is conducted using a confusion matrix. Based on confusion matrix analysis, accuracy, precision, recall, and F1-score are calculated. Among the five models, SVM achieves the highest accuracy at 87%, followed by RF and K-NN. This indicates that the SVM model performs better in binary classification. Ground truth analysis is also conducted using the QGIS platform, which visualizes the classification results, with SVM providing the best visualization. |
| format | Article |
| id | doaj-art-b4982cdf6be84275a2e044a184085676 |
| institution | Kabale University |
| issn | 2655-8564 2685-9432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Universitas Sanata Dharma |
| record_format | Article |
| series | International Journal of Applied Sciences and Smart Technologies |
| spelling | doaj-art-b4982cdf6be84275a2e044a1840856762025-08-20T03:26:34ZengUniversitas Sanata DharmaInternational Journal of Applied Sciences and Smart Technologies2655-85642685-94322025-06-017114515810.24071/ijasst.v7i1.116083956Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, ThailandNi Putu Nita Nathalia0Gede Andra Rizqy Wijaya1Kadek Yota Ernanda Aryanto2Ni Putu Novita Puspa Dewi3Putu Hendra Saputra4Ni Putu Karisma Dewi5Mellisa Damayanti6Kadek Losinanda Prawira7Universitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaSatellite imagery is utilized in various fields, one of which is land use and land cover (LULC) analysis. This study aims to classify water bodies using machine learning models such as SVM, K-NN, RF, CART, and GNB. The data source is obtained from the Google Earth Engine (GEE) platform using Sentinel-2 Level-2A satellite imagery, with a dataset of 5,514 data points per year. The Pixel-Based approach is used as the main method for data extraction, while CRISP-DM is applied as a structured methodology for data management. The parameter indices used include the BSI, NDBI, MNDWI, NDVI and AWEIsh. The results of these calculations serve as dataset features for training algorithms in the model development and training process. Each model has its own parameters, making parameter selection crucial in the training process. Model evaluation is conducted using a confusion matrix. Based on confusion matrix analysis, accuracy, precision, recall, and F1-score are calculated. Among the five models, SVM achieves the highest accuracy at 87%, followed by RF and K-NN. This indicates that the SVM model performs better in binary classification. Ground truth analysis is also conducted using the QGIS platform, which visualizes the classification results, with SVM providing the best visualization.https://e-journal.usd.ac.id/index.php/IJASST/article/view/11608 |
| spellingShingle | Ni Putu Nita Nathalia Gede Andra Rizqy Wijaya Kadek Yota Ernanda Aryanto Ni Putu Novita Puspa Dewi Putu Hendra Saputra Ni Putu Karisma Dewi Mellisa Damayanti Kadek Losinanda Prawira Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand International Journal of Applied Sciences and Smart Technologies |
| title | Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand |
| title_full | Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand |
| title_fullStr | Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand |
| title_full_unstemmed | Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand |
| title_short | Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand |
| title_sort | comparison of svm k nn rf cart and gnb algorithms for water bodies detection using sentinel 2 level 2a imagery in nakhon pathom thailand |
| url | https://e-journal.usd.ac.id/index.php/IJASST/article/view/11608 |
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