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.
| Published in | Journal of Water Resources and Ocean Science (Volume 5, Issue 6) |
| DOI | 10.11648/j.wros.20160506.12 |
| Page(s) | 87-92 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Dissolved Oxygen, Dynamic Change Prediction, GM (1,1) Prediction Model, BP Neural Network, Combination Prediction Model
| [1] | Julong D. Introduction to grey system theory. The Journal of grey system, 1989, 1(1): 1-24. |
| [2] | Sun Shijun, Li Rui, Xie Xinmin, Liu Zuoxin. Application of BP network in real-time forecast of drought period surface runoff in guanyinge reservoir of Liaoning. Journal of Shenyang Agricultural University, 2004, 35(5-6):504-506 |
| [3] | Wang Xiaoping, Sun Jiyang, Jin Xin. Prediction of water quality index in Qiantang River based on BP neural network model. Journal of Zhejiang University(Engineering Science), 2007, 41(2): 361-364. |
| [4] | Yu Yingjie, Jiang Weigang, Xu Mingfang. Prediction of chlorophyll a by BP neural network model based on PSO algorithm. Research of environmental sciences, 2011, 24(5): 526-532. |
| [5] | Bates J M, Granger C W J. The combination of forecasts. Journal of the Operational Research Society, 1969, 20(4): 451-468. |
| [6] | Ju Qin, Hao Zhenchun, Liu Jie. Prediction of river quality using the united gray neural network model. Journal of Hebei University of Engineering(Natural Science Edition), 2007, 24(3): 26-28. |
| [7] | Guo Lanlan, Zou Zhihong, An Yan. Study on grey model combined with artificial neural networks model for water quality forecast. Mathematics in practice and theory, 2015 (5): 89-93. |
| [8] | Zhou Bo, Zhou Hui. Center approach grey BP neural network prediction model for water quality. Haihe water resources, 2011 (6): 34-37. |
| [9] | Wang Jian. Research on prediction of water resource based on improved combination neural network. Computer Science. 2016, 1. |
| [10] | Si Xin. Neural network model in method of prediction. Forecasting, 1998, 2: 32-35. |
| [11] | Liu Rentao, Fu Qiang, Feng Yan, Gai Zhaomei, Li Guoliang, Li Weiye. Grey BP neural networks model based on RAGA and its application in groundwater dynamic prediction of the Sanjiang plain. System Engineering Theory and Practice, 2008, 28(5): 171-176. |
| [12] | David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 1986, 323: 533-536. |
| [13] | Tian Jianping, Cao Dongwei, Li Hainan. Application of LM-BP Neural Network in Water Quality Prediction for Yu Qiao Reservoir. Water resources in formatization, 2010 (3): 31-34. |
| [14] | Zhang Ying, Gao Qianqian. Comprehensive prediction model of water quality based on Grey Model and Fuzzy Neural Network. Chinese Journal of environmental Engineering, 2015 (2): 537-545. |
APA Style
Wang Cao, Jingya Sun, M. V. Subrahmanyam, Feng Gui. (2016). A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models. Journal of Water Resources and Ocean Science, 5(6), 87-92. https://doi.org/10.11648/j.wros.20160506.12
ACS Style
Wang Cao; Jingya Sun; M. V. Subrahmanyam; Feng Gui. A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models. J. Water Resour. Ocean Sci. 2016, 5(6), 87-92. doi: 10.11648/j.wros.20160506.12
AMA Style
Wang Cao, Jingya Sun, M. V. Subrahmanyam, Feng Gui. A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models. J Water Resour Ocean Sci. 2016;5(6):87-92. doi: 10.11648/j.wros.20160506.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.wros.20160506.12,
author = {Wang Cao and Jingya Sun and M. V. Subrahmanyam and Feng Gui},
title = {A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models},
journal = {Journal of Water Resources and Ocean Science},
volume = {5},
number = {6},
pages = {87-92},
doi = {10.11648/j.wros.20160506.12},
url = {https://doi.org/10.11648/j.wros.20160506.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20160506.12},
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.},
year = {2016}
}
TY - JOUR T1 - A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models AU - Wang Cao AU - Jingya Sun AU - M. V. Subrahmanyam AU - Feng Gui Y1 - 2016/11/18 PY - 2016 N1 - https://doi.org/10.11648/j.wros.20160506.12 DO - 10.11648/j.wros.20160506.12 T2 - Journal of Water Resources and Ocean Science JF - Journal of Water Resources and Ocean Science JO - Journal of Water Resources and Ocean Science SP - 87 EP - 92 PB - Science Publishing Group SN - 2328-7993 UR - https://doi.org/10.11648/j.wros.20160506.12 AB - 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. VL - 5 IS - 6 ER -
Email: