Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing w...

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Autores principales:	Botao Chen, Xi Mu, Peng Chen, Biao Wang, Jaewan Choi, Honglyun Park, Sheng Xu, Yanlan Wu, Hui Yang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	UAV remote sensing Water quality inversion Machine learning Urban river Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/51aee175aaa149f6a2b007657c26c64b
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id	oai:doaj.org-article:51aee175aaa149f6a2b007657c26c64b
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spelling	oai:doaj.org-article:51aee175aaa149f6a2b007657c26c64b2021-12-04T04:33:25ZMachine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data1470-160X10.1016/j.ecolind.2021.108434https://doaj.org/article/51aee175aaa149f6a2b007657c26c64b2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21010992https://doaj.org/toc/1470-160XUrban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing water quality monitoring in urban rivers. In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism learning and semantic feature analysis. The results show that the coefficient of determination (R2) of GA_XGBoost algorithm for chlorophyll a (Chla), total phosphorous (TP), total nitrogen (TN), ammonia–nitrogen (NH3-N) and turbidity (TUB) is 0.855, 0.699, 0.787, 0.694, and 0.597, respectively, indicating a high precision and the predicted results are consistent with the measured data. Meanwhile, this paper compares the GA_XGBoost model with other algorithms: Deep Neural Network (DNN), Random Forest, genetic algorithm_RandomForest (GA_RandomForest), adaptive boosting (AdaBoost) and genetic algorithm_adaptive boosting (GA_AdaBoost), and the performance of the GA_XGBoost model is better. At the same time, data from different periods have been added to verify the model’s applicability. Moreover, based on the inversion results, analyze from the point of view of point source pollution, non-point source pollution, etc., to further investigate the influencing factors that cause urban river pollution. The current method has important practical significance for promoting the intelligent and automatic level of water environment monitoring technology in ecological environmental protection and urban water resources protection.Botao ChenXi MuPeng ChenBiao WangJaewan ChoiHonglyun ParkSheng XuYanlan WuHui YangElsevierarticleUAV remote sensingWater quality inversionMachine learningUrban riverEcologyQH540-549.5ENEcological Indicators, Vol 133, Iss , Pp 108434- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAV remote sensing Water quality inversion Machine learning Urban river Ecology QH540-549.5
spellingShingle	UAV remote sensing Water quality inversion Machine learning Urban river Ecology QH540-549.5 Botao Chen Xi Mu Peng Chen Biao Wang Jaewan Choi Honglyun Park Sheng Xu Yanlan Wu Hui Yang Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
description	Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing water quality monitoring in urban rivers. In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism learning and semantic feature analysis. The results show that the coefficient of determination (R2) of GA_XGBoost algorithm for chlorophyll a (Chla), total phosphorous (TP), total nitrogen (TN), ammonia–nitrogen (NH3-N) and turbidity (TUB) is 0.855, 0.699, 0.787, 0.694, and 0.597, respectively, indicating a high precision and the predicted results are consistent with the measured data. Meanwhile, this paper compares the GA_XGBoost model with other algorithms: Deep Neural Network (DNN), Random Forest, genetic algorithm_RandomForest (GA_RandomForest), adaptive boosting (AdaBoost) and genetic algorithm_adaptive boosting (GA_AdaBoost), and the performance of the GA_XGBoost model is better. At the same time, data from different periods have been added to verify the model’s applicability. Moreover, based on the inversion results, analyze from the point of view of point source pollution, non-point source pollution, etc., to further investigate the influencing factors that cause urban river pollution. The current method has important practical significance for promoting the intelligent and automatic level of water environment monitoring technology in ecological environmental protection and urban water resources protection.
format	article
author	Botao Chen Xi Mu Peng Chen Biao Wang Jaewan Choi Honglyun Park Sheng Xu Yanlan Wu Hui Yang
author_facet	Botao Chen Xi Mu Peng Chen Biao Wang Jaewan Choi Honglyun Park Sheng Xu Yanlan Wu Hui Yang
author_sort	Botao Chen
title	Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
title_short	Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
title_full	Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
title_fullStr	Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
title_full_unstemmed	Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data
title_sort	machine learning-based inversion of water quality parameters in typical reach of the urban river by uav multispectral data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/51aee175aaa149f6a2b007657c26c64b
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_version_	1718372965940199424

Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

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