Volume 15 Issue 3
Aug.  2022
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Yu-jun Bao, Chang-ying Ji, Bing Zhang. 2022: Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T–S fuzzy neural network. Water Science and Engineering, 15(3): 210-217. doi: 10.1016/j.wse.2022.06.001
Citation: Yu-jun Bao, Chang-ying Ji, Bing Zhang. 2022: Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T–S fuzzy neural network. Water Science and Engineering, 15(3): 210-217. doi: 10.1016/j.wse.2022.06.001

Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T–S fuzzy neural network

doi: 10.1016/j.wse.2022.06.001
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This work was supported by the Natural Science Foundation of Changzhou City, China (Grants No. CE20195026 and CE20205031), the Teaching Steering Committee of Electronics Information Specialty in Colleges and Universities of the Ministry of Education (Grant No. 2020-YB-42), and the Jiangsu Overseas Visiting Scholar Program for University Prominent Young and Middle-Aged Teachers and Presidents.

  • Received Date: 2021-05-25
  • Accepted Date: 2022-03-10
  • Rev Recd Date: 2022-03-10
  • Available Online: 2022-08-24
  • Dissolved oxygen (DO) content is an important index of river water quality. Water quality sensors have been used in China for urban river water monitoring and DO content prediction. However, water quality sensors are expensive and difficult to maintain, and have a short operation period and difficult to maintain. This study developed a scientific and accurate method for prediction of DO content changes using fish school features. The behavioral features of the Carassius auratus fish school were described using two-dimensional fish school images. The degree of DO content decline was graded into five levels, and the corresponding numerical ranges of cluster characteristic parameters were determined by considering the opinions of ichthyologists. Finally, the variation of DO content was predicted using the characteristic parameters of the fish school and the multiple-input single-output Takagi–Sugeno fuzzy neural network. The prediction results were basically consistent with the actual variations of DO content. Therefore, it is feasible to use the behavioral features of the fish school to dynamically predict the level of DO content in water, and this method is especially suitable for prediction of sharp decline of DO content in a relatively short time.

     

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