Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea

2007 ◽  
Vol 34 (16) ◽  
pp. 2161-2175 ◽  
Author(s):  
Bang-Fuh Chen ◽  
Han-Der Wang ◽  
Chih-Chun Chu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
South China Sea ◽  
South China ◽  
China Sea ◽  
Network Analyses ◽  
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.

scholarly journals Retrieving monthly and interannual pH<sub>T</sub> on the East China Sea shelf using an artificial neural network: ANN-pH<sub>T</sub>-v1

2019 ◽  
Author(s):  
Xiaoshuang Li ◽  
Richard Bellerby ◽  
Jianzhong Ge ◽  
Philip Wallhead ◽  
Jing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Column ◽  
East China Sea ◽  
East China ◽  
Ann Model ◽  
The East China Sea ◽  
China Sea ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. While our understanding of pH dynamics has strongly progressed for open ocean regions, for marginal seas such as the East China Sea (ECS) progress has been constrained by limited observations and complex interactions between biological, physical, and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high quality instrumentation and according to standard practices. In order to predict water column total scale pH (pHT) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from 3 cruises in 2018, and showed a root mean square error accuracy of 0.04. A weight analysis of the ANN model variables suggested that DO, S, T were the most important predictor variables. Monthly water column pHT for the period 2000-2016 was retrieved using T, S, DO, N, P, and Si from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM).

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