Assessment for water quality by artificial neural network in Daya Bay, South China Sea

Ecotoxicology ◽  
2015 ◽  
Vol 24 (7-8) ◽  
pp. 1632-1642 ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Ji-Dong Gu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
South China Sea ◽  
South China ◽  
Daya Bay ◽  
China Sea ◽  
Artificial Neural

scholarly journals Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network

2021 ◽  
Vol 9 (5) ◽  
pp. 488
Author(s):  
Jin Huang ◽  
Yu Luo ◽  
Jian Shi ◽  
Xin Ma ◽  
Qian-Qian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
South China Sea ◽  
Network Model ◽  
South China ◽  
Sound Speed ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Artificial Neural

Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120°E, 6°–8°N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea

2010 ◽  
Vol 60 (6) ◽  
pp. 852-860 ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Cui-Ci Sun ◽  
Haili Wang ◽  
Jun-De Dong ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
South China Sea ◽  
Coastal Water ◽  
South China ◽  
Daya Bay ◽  
Coastal Water Quality ◽  
China Sea ◽  
Analysis Methods ◽  
Statistical Analysis Methods

scholarly journals Investigation of Spatial and Temporal Trends in Water Quality in Daya Bay, South China Sea

2011 ◽  
Vol 8 (6) ◽  
pp. 2352-2365 ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Jun-De Dong ◽  
Cui-Ci Sun ◽  
Yu-Tu Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
South China Sea ◽  
South China ◽  
Temporal Trends ◽  
Daya Bay ◽  
China Sea ◽  
Spatial And Temporal Trends

Identification of anthropogenic effects and seasonality on water quality in Daya Bay, South China Sea

2009 ◽  
Vol 90 (10) ◽  
pp. 3082-3090 ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Cui-Ci Sun ◽  
Haili Wang ◽  
Jun-De Dong ◽  
...  
Keyword(s):  
Water Quality ◽  
South China Sea ◽  
South China ◽  
Anthropogenic Effects ◽  
Daya Bay ◽  
China Sea

Using ciliates to monitor different aquatic environments in Daya Bay, South China Sea

2016 ◽  
Vol 94 (4) ◽  
pp. 265-273 ◽  
Author(s):  
Fengxia Wu ◽  
Jianrong Huang ◽  
Ming Dai ◽  
Huaxue Liu ◽  
Honghui Huang
Keyword(s):  
Water Quality ◽  
South China Sea ◽  
South China ◽  
Dominant Species ◽  
Marine Water ◽  
Daya Bay ◽  
China Sea ◽  
Mesodinium Rubrum ◽  
Nitrogen Nutrients ◽  
Planktonic Ciliate

Spatial patterns of planktonic ciliate communities were studied to monitor marine water quality during winter, spring, summer, and autumn of 2014 in Daya Bay, South China Sea. A total of 41 species, with eight dominant species, were identified. Multivariate and univariate analyses demonstrated that spatial taxonomic pattern of planktonic ciliate communities was significantly correlated with environmental condition; ciliate abundance, species diversity, and species richness were significantly correlated with NO3– and salinity or temperature. Additionally, two dominant species (Tintinnopsis minuta Wailes, 1925 and Tintinnopsis beroidea Stein, 1867) were significantly positively correlated with nutrients, particularly with nitrogen nutrients. Three dominant species (Strombidium conicum (Lohmann, 1908) Wulff, 1919; Spirotontonia turbinata (Song & Bradbury, 1998) Agatha, 2004; Laboea strobila Lohmann, 1908) showed more sensitivity to salinity, whereas Mesodinium rubrum (Lohmann, 1908) was significantly correlated with temperature. Our findings suggest that planktonic ciliate communities can be considered a favorable bioindicator of marine water quality.

Seasonal and spatial variations of water quality and trophic status in Daya Bay, South China Sea

2016 ◽  
Vol 112 (1-2) ◽  
pp. 341-348 ◽  
Author(s):  
Mei-Lin Wu ◽  
You-Shao Wang ◽  
Yu-Tu Wang ◽  
Fu-Lin Sun ◽  
Cui-Ci Sun ◽  
...  
Keyword(s):  
Water Quality ◽  
South China Sea ◽  
South China ◽  
Trophic Status ◽  
Spatial Variations ◽  
Daya Bay ◽  
China Sea ◽  
Seasonal And Spatial Variations

scholarly journals A Study on Comprehensive Evaluation of Multiple-depth Sea Water Quality in the South China Sea

2021 ◽  
Vol 809 (1) ◽  
pp. 012013
Author(s):  
Xiaolei Ma ◽  
Mengshan Duan ◽  
Duomo Duan ◽  
Jinquan Qiu ◽  
Junrui Cao
Keyword(s):  
Water Quality ◽  
South China Sea ◽  
South China ◽  
Sea Water ◽  
Comprehensive Evaluation ◽  
The South China Sea ◽  
The South ◽  
China Sea
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