The Dissolved Oxygen Prediction Method Based on Neural Network. (9th October 2017)

• Record Type: Journal Article
• Authors: Xiao, Zhong; Peng, Lingxi; Chen, Yi; Liu, Haohuai; Wang, Jiaqing; Nie, Yangang
• Other Names: Zhong Junpei Academic Editor.
• Is Part Of: Complexity. Volume 2017(2017)
• Publication Date: 2017-10-09
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2017/4967870
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The dissolved oxygen (DO) is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture's dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF), autoregression (AR), grey model (GM), and support vector machines (SVM), the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.