Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam
For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, grad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/10/1552 |
| _version_ | 1797494540643336192 |
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| author | Dao Nguyen Khoi Nguyen Trong Quan Do Quang Linh Pham Thi Thao Nhi Nguyen Thi Diem Thuy |
| author_facet | Dao Nguyen Khoi Nguyen Trong Quan Do Quang Linh Pham Thi Thao Nhi Nguyen Thi Diem Thuy |
| author_sort | Dao Nguyen Khoi |
| collection | DOAJ |
| description | For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R<sup>2</sup> and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R<sup>2</sup> = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management. |
| first_indexed | 2024-03-10T01:35:47Z |
| format | Article |
| id | doaj.art-b69461f0478e46bbb9762f79cb8c7694 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T01:35:47Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-b69461f0478e46bbb9762f79cb8c76942023-11-23T13:34:05ZengMDPI AGWater2073-44412022-05-011410155210.3390/w14101552Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, VietnamDao Nguyen Khoi0Nguyen Trong Quan1Do Quang Linh2Pham Thi Thao Nhi3Nguyen Thi Diem Thuy4Faculty of Environment, University of Science, Ho Chi Minh City 700000, VietnamInstitute for Computational Science and Technology, Ho Chi Minh City 700000, VietnamInstitute of Hydrometeorology, Oceanology and Environment, Ho Chi Minh City 700000, VietnamInstitute for Computational Science and Technology, Ho Chi Minh City 700000, VietnamFaculty of Environment, University of Science, Ho Chi Minh City 700000, VietnamFor effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R<sup>2</sup> and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R<sup>2</sup> = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.https://www.mdpi.com/2073-4441/14/10/1552La Buong Rivermachine learning algorithmssurface water qualitywater quality index (WQI) |
| spellingShingle | Dao Nguyen Khoi Nguyen Trong Quan Do Quang Linh Pham Thi Thao Nhi Nguyen Thi Diem Thuy Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam Water La Buong River machine learning algorithms surface water quality water quality index (WQI) |
| title | Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam |
| title_full | Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam |
| title_fullStr | Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam |
| title_full_unstemmed | Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam |
| title_short | Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam |
| title_sort | using machine learning models for predicting the water quality index in the la buong river vietnam |
| topic | La Buong River machine learning algorithms surface water quality water quality index (WQI) |
| url | https://www.mdpi.com/2073-4441/14/10/1552 |
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