Volume 5, Issue 6, December 2016, Page: 87-92
A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models
Wang Cao, Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, , China; Marine Biology Institute, Shantou University, Shantou, China
Jingya Sun, Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, , China
M. V. Subrahmanyam, Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, , China
Feng Gui, Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, , China
Received: Oct. 11, 2016;       Accepted: Oct. 20, 2016;       Published: Nov. 18, 2016
DOI: 10.11648/j.wros.20160506.12      View  2829      Downloads  71
Abstract
Dissolved oxygen (DO) is a key water quality parameter and dynamic change prediction of water quality can provide a necessary assistance to solve the marine pollution problem. In this study, DO concentration data were collected from the buoy near Aoshan Island, Zhoushan, China. Based on DO concentration analysis, three prediction model were established, which includes Grey prediction model (GM (1,1)), back propagation(BP) neural network prediction model and the combination of GM-BP neural network prediction model. All three models have high fitting degree and the average relative error for each model is 9.1482%, 1.8940% and 0.2195% respectively. Hence, the combination of GM-BP neural network prediction model has highest accuracy than BP neural network prediction modeland GM (1,1) prediction model.Combination of prediction model has more advantages than a single prediction model and it is possible to improve the accuracy of prediction for better results.
Keywords
Dissolved Oxygen, Dynamic Change Prediction, GM (1,1) Prediction Model, BP Neural Network, Combination Prediction Model
To cite this article
Wang Cao, Jingya Sun, M. V. Subrahmanyam, Feng Gui, A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models, Journal of Water Resources and Ocean Science. Vol. 5, No. 6, 2016, pp. 87-92. doi: 10.11648/j.wros.20160506.12
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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