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Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

1
Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Guangzhou 510006, China
3
State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen 518055, China
4
Guangzhou Franzero Water Technology Co., Ltd., Guangzhou 510663, China
*
Author to whom correspondence should be addressed.
Academic Editors: Bommanna Krishnappan, Kairong Lin, Fan Lu and Tian Lan
Water 2021, 13(20), 2844; https://doi.org/10.3390/w13202844
Received: 3 September 2021 / Revised: 2 October 2021 / Accepted: 3 October 2021 / Published: 12 October 2021
(This article belongs to the Special Issue Urban Water Security and Sustainable Development)
In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts. View Full-Text
Keywords: eutrophication; multi-temporal remote sensing image; back-propagation; Dashahe reservoir; water quality parameter inversion eutrophication; multi-temporal remote sensing image; back-propagation; Dashahe reservoir; water quality parameter inversion
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MDPI and ACS Style

He, Y.; Gong, Z.; Zheng, Y.; Zhang, Y. Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir. Water 2021, 13, 2844. https://doi.org/10.3390/w13202844

AMA Style

He Y, Gong Z, Zheng Y, Zhang Y. Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir. Water. 2021; 13(20):2844. https://doi.org/10.3390/w13202844

Chicago/Turabian Style

He, Yanhu, Zhenjie Gong, Yanhui Zheng, and Yuanbo Zhang. 2021. "Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir" Water 13, no. 20: 2844. https://doi.org/10.3390/w13202844

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