Journal of Water Resources and Ocean Science 2016; 5(6): 87-92 http://www.sciencepublishinggroup.com/j/wros doi: 10.11648/j.wros.20160506.12 ISSN: 2328-7969 (Print); ISSN: 2328-7993 (Online) A Comparison Study of Seawater Dissolved Oxygen Using Dynamic Changes Prediction Models Wang Cao 1, 2 , Jingya Sun 1 , M. V. Subrahmanyam 1 , Feng Gui 1, * 1 Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, China 2 Marine Biology Institute, Shantou University, Shantou, China Email address: 1263932751@qq.com (Wang Cao), sjy2005@zjou.edu.cn (Jingya Sun), mvsm.au@gmail.com (M. V. Subrahmanyam), guifeng77@vip.126.com (Feng Gui) * Corresponding author 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 Received: October 11, 2016; Accepted: October 20, 2016; Published: November 18, 2016 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 model and 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 1. Introduction The Marine Environment Bulletin data was indicated that large areas of China’s coastal waters were under an unhealthy state for several years. However, besides the regular marine monitoring data, dynamic change prediction of water quality can provide a necessary assistance to solve the marine pollution problem. In recent years, several studies have been carried out worldwide on water quality prediction models to provide the necessary theoretical basis for marine environment protection. Traditional water quality prediction models include water quality simulation, historical valuation method, linear regression, climatologically mean, the gray prediction (Julong 1989)and so on. These methods cannot have high precision and fitness when dealing with uncertain fuzzy dynamic changes of water quality. Artificial neural network (ANN) has four characteristics-nonlinear, non-limiting, high qualitative and non-convexity. ANN is powerful adaptive, self-organizing, self-learning ability and the ability of infinite nonlinear function approximation. Also ANN is a powerful tool to handle and excavate data relationships and establish prediction model. In recent years, several scientists have been working on prediction of water quality especially in China. By established a reasonable low flow back propagation (BP) neural network prediction model, which has a high degree of fitness and improved accuracy (Sun et al 2004). Based on BP neural network, which was used levenberg-marquard (LM) algorithm, predicted the water quality of Qiantang River with a maximum error of 11.7% and the mean error of 4.3% (Wang et al 2007). In addition, based on the limitation of traditional neural networks, a new neural network model was proposed by correcting the artificial neural network weight algorithm, optimize neural network structure and global convergence algorithm (Yu et al 2011). Since Bates and Granger (1969) proposed a combined prediction model for the first time. Several researchers pointed out that a combination of two or more prediction models can increase the performance,