Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms
Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the rem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/22/4662 |
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| author | Zhi Qiao Siyang Sun Qun’ou Jiang Ling Xiao Yunqi Wang Haiming Yan |
| author_facet | Zhi Qiao Siyang Sun Qun’ou Jiang Ling Xiao Yunqi Wang Haiming Yan |
| author_sort | Zhi Qiao |
| collection | DOAJ |
| description | Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R<sup>2</sup> reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir. |
| first_indexed | 2024-03-10T05:06:01Z |
| format | Article |
| id | doaj.art-62a5b3c817ba4cf0b68eafd8a86b9ef2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:06:01Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-62a5b3c817ba4cf0b68eafd8a86b9ef22023-11-23T01:21:11ZengMDPI AGRemote Sensing2072-42922021-11-011322466210.3390/rs13224662Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning AlgorithmsZhi Qiao0Siyang Sun1Qun’ou Jiang2Ling Xiao3Yunqi Wang4Haiming Yan5School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Land Resources and Urban & Rural Planning, Hebei GEO University, Shijiazhuang 050031, ChinaSome essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R<sup>2</sup> reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.https://www.mdpi.com/2072-4292/13/22/4662machine learning algorithmretrieval modelremote sensing datatotal phosphorus concentrationMiyun Reservoir |
| spellingShingle | Zhi Qiao Siyang Sun Qun’ou Jiang Ling Xiao Yunqi Wang Haiming Yan Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms Remote Sensing machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir |
| title | Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms |
| title_full | Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms |
| title_fullStr | Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms |
| title_full_unstemmed | Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms |
| title_short | Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms |
| title_sort | retrieval of total phosphorus concentration in the surface water of miyun reservoir based on remote sensing data and machine learning algorithms |
| topic | machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir |
| url | https://www.mdpi.com/2072-4292/13/22/4662 |
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