Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
The Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consu...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4130 |
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| author | Jiawei Gao Bochao Chen Su-Kit Tang |
| author_facet | Jiawei Gao Bochao Chen Su-Kit Tang |
| author_sort | Jiawei Gao |
| collection | DOAJ |
| description | The Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consuming and labor-intensive, making real-time and efficient water quality monitoring challenging. To address this issue, this study proposes a Raspberry Pi-based multi-sensor system for rapid water quality detection and improved monitoring efficiency. This system integrates multiple sensors to measure key water quality parameters, such as pH, total dissolved solids (TDSs), temperature, and turbidity, while recording data in real-time. The data were continuously collected over a period of five months (July to November 2024). The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. Among these models, Random Forest exhibited the best overall performance, achieving an accuracy of 98.10% and an F1 score of 98.99%. These results show that the dataset demonstrates high reliability in anomaly detection and classification tasks, accurately identifying deviations in water quality. This approach not only enhances the efficiency of water quality monitoring but also provides technological support for urban drinking water safety management. |
| format | Article |
| id | doaj-art-c635d884d1ff4fcabbf0bcf5edeea708 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c635d884d1ff4fcabbf0bcf5edeea7082025-08-20T03:14:19ZengMDPI AGApplied Sciences2076-34172025-04-01158413010.3390/app15084130Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in MacaoJiawei Gao0Bochao Chen1Su-Kit Tang2Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao 999078, ChinaThe Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consuming and labor-intensive, making real-time and efficient water quality monitoring challenging. To address this issue, this study proposes a Raspberry Pi-based multi-sensor system for rapid water quality detection and improved monitoring efficiency. This system integrates multiple sensors to measure key water quality parameters, such as pH, total dissolved solids (TDSs), temperature, and turbidity, while recording data in real-time. The data were continuously collected over a period of five months (July to November 2024). The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. Among these models, Random Forest exhibited the best overall performance, achieving an accuracy of 98.10% and an F1 score of 98.99%. These results show that the dataset demonstrates high reliability in anomaly detection and classification tasks, accurately identifying deviations in water quality. This approach not only enhances the efficiency of water quality monitoring but also provides technological support for urban drinking water safety management.https://www.mdpi.com/2076-3417/15/8/4130water quality monitoringMacaoanomaly detectiondataset |
| spellingShingle | Jiawei Gao Bochao Chen Su-Kit Tang Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao Applied Sciences water quality monitoring Macao anomaly detection dataset |
| title | Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao |
| title_full | Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao |
| title_fullStr | Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao |
| title_full_unstemmed | Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao |
| title_short | Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao |
| title_sort | water quality monitoring a water quality dataset from an on site study in macao |
| topic | water quality monitoring Macao anomaly detection dataset |
| url | https://www.mdpi.com/2076-3417/15/8/4130 |
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