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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Autores principales:	Zhi Qiao, Siyang Sun, Qun’ou Jiang, Ling Xiao, Yunqi Wang, Haiming Yan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir Science Q
Acceso en línea:	https://doaj.org/article/62a5b3c817ba4cf0b68eafd8a86b9ef2
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spelling	oai:doaj.org-article:62a5b3c817ba4cf0b68eafd8a86b9ef22021-11-25T18:55:12ZRetrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms10.3390/rs132246622072-4292https://doaj.org/article/62a5b3c817ba4cf0b68eafd8a86b9ef22021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4662https://doaj.org/toc/2072-4292Some 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.Zhi QiaoSiyang SunQun’ou JiangLing XiaoYunqi WangHaiming YanMDPI AGarticlemachine learning algorithmretrieval modelremote sensing datatotal phosphorus concentrationMiyun ReservoirScienceQENRemote Sensing, Vol 13, Iss 4662, p 4662 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir Science Q
spellingShingle	machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir Science Q 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
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.
format	article
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
title	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_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_sort	retrieval of total phosphorus concentration in the surface water of miyun reservoir based on remote sensing data and machine learning algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/62a5b3c817ba4cf0b68eafd8a86b9ef2
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Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms

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