A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

Author:

Sun Jiajun123ORCID,Li Dashe123,Fan Deming4

Affiliation:

1. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China

2. Key Laboratory of Intelligent Information Processing, Shandong Technology and Business University, Yantai, Shandong, China

3. Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, Shandong, China

4. School of Computer Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong, China

Abstract

A challenge of achieving intelligent marine ranching is the prediction of dissolved oxygen (DO). DO directly reflects marine ranching environmental conditions. Through accurate DO predictions, timely human intervention can be made in marine pasture water environments to avoid problems such as reduced yields or marine crop death due to low oxygen concentrations in the water. We use an enhanced semi-naive Bayes model for prediction based on an analysis of DO data from marine pastures in northeastern China from the past three years. Based on the semi-naive Bayes model, this paper takes the possible values of a DO difference series as categories, counts the possible values of the first-order difference series and the difference series of the interval before each possible value, and selects the most probable difference series value at the next moment. The prediction accuracy is optimized by adjusting the attribute length and frequency threshold of the difference sequence. The enhanced semi-naive Bayes model is compared with LSTM, RBF, SVR and other models, and the error function and Willmott’s index of agreement are used to evaluate the prediction accuracy. The experimental results show that the proposed model has high prediction accuracy for DO attributes in marine pastures.

Funder

CERNET Innovation Project

Yantai Science and Technology Innovation Development Project

Key R&D Program of Shandong Province

Publisher

PeerJ

Subject

General Computer Science

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