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Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters
Article

Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

1
School of Municipal and Surveying Engineering, Design Institute Co., Ltd., Hunan City University, Yiyang 413000, China
2
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
3
School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
4
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Academic Editor: Alessandro Bergamasco
Water 2021, 13(5), 664; https://doi.org/10.3390/w13050664
Received: 31 December 2020 / Revised: 25 February 2021 / Accepted: 27 February 2021 / Published: 28 February 2021
For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration. View Full-Text
Keywords: convolutional neural network; chlorophyll-a; Dongting Lake convolutional neural network; chlorophyll-a; Dongting Lake
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MDPI and ACS Style

Xue, Y.; Zhu, L.; Zou, B.; Wen, Y.-m.; Long, Y.-h.; Zhou, S.-l. Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model. Water 2021, 13, 664. https://doi.org/10.3390/w13050664

AMA Style

Xue Y, Zhu L, Zou B, Wen Y-m, Long Y-h, Zhou S-l. Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model. Water. 2021; 13(5):664. https://doi.org/10.3390/w13050664

Chicago/Turabian Style

Xue, Yun, Lei Zhu, Bin Zou, Yi-min Wen, Yue-hong Long, and Song-lin Zhou. 2021. "Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model" Water 13, no. 5: 664. https://doi.org/10.3390/w13050664

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