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

He, Yanhu1,2; Gong, Zhenjie1; Zheng, Yanhui3; Zhang, Yuanbo4

2021-10-01

10.3390/w13202844

WATER   影响因子和分区

2073-4441

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.

eutrophication multi-temporal remote sensing image back-propagation Dashahe reservoir water quality parameter inversion

SCI ; EI

英语

通讯

National Natural Science Foundation of China[51979043]

Environmental Sciences & Ecology ; Water Resources

Environmental Sciences ; Water Resources

WOS:000716353600001

MDPI

Web of Science

1.Guangdong Univ Technol, Inst Environm & Ecol Engn, Guangzhou 510006, Peoples R China
2.Guangdong Prov Key Lab Water Qual Improvement & E, Guangzhou 510006, Peoples R China
3.South Univ Sci & Technol China, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen 518055, Peoples R China
4.Guangzhou Franzero Water Technol Co Ltd, Guangzhou 510663, Peoples R China

He, Yanhu,Gong, Zhenjie,Zheng, Yanhui,et al. Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir[J]. WATER,2021,13(20).

He, Yanhu,Gong, Zhenjie,Zheng, Yanhui,&Zhang, Yuanbo.(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(20).

He, Yanhu,et al."Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir".WATER 13.20(2021).