Volume 15 Issue 3
Aug.  2022
Turn off MathJax
Article Contents
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

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.


  • loading
  • [1]
    Bao, Y.J., Ji, C.Y., Zhang, B., Gu, J.L., 2018. Representation of freshwater aquaculture fish behavior in low dissolved oxygen condition based on 3D computer vision. Modern Physics Letters B 32(34-36), 1840090. https://doi.org/10.1142/S0217984918400900
    Cao, S., Zhou, L., Zhang, Z., 2021. Prediction model of dissolved oxygen in aquaculture based on improved long short-term memory neural network. Transactions of the Chinese Society of Agricultural Engineering 37(14), 235-242 (in Chinese). https://doi.org/10.11975/j.issn.1002-6819.2021.14.027
    Chew, B.F., Eng, H.L., Thida, M., 2009. Vision-based real-time monitoring on the behavior of fish school. In: Proceeding of IAPR Conference on Machine Vision Applications. MVA Organization, Yokohama, pp. 90-93
    Delcourt, J., Becco, C., Vandewalle, N., Poncin, P., 2009. A video multitracking system for quantification of individual behavior in a large fish shoal: Advantages and limits. Behavior Research Methods 41, 228-235. https://doi.org/10.3758/BRM.41.1.228
    Heddam, S., 2014. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): A comparative study. Environmental Monitoring and Assessment 186, 597-619. https://doi.org/10.1007/s10661-013-3402-1
    Hou, Y., 2014. Traffic flow prediction based on improved T-S fuzzy neural network. Journal of Frontiers of Computer Science and Technology 8(1), 121-126 (in Chinese). https://doi.org/10.3778/j.issn.1673-9418.1309008
    Li, Q., Zhang, P., Peng, F., 2019. Research progress and preliminary plan of national water quality forecasting and alarming system. Environmental Monitoring in China 35(1), 8-16 (in Chinese). https://doi.org/10.19316/j.issn.1002-6002.2019.01.02
    Lin, L., 2016. Assessment and treatment of water pollution in Yangtze River Delta. Environmental Protection 44(17), 41-45 (in Chinese). https://doi.org/10.14026/j.cnki.0253-9705.2016.17.008
    Liu, S., Xu, L., Li, D., Li, Q., Jiang, Y., Tai, H., Zeng, L., 2013. Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particles swarm optimization. Computers and Electronics in Agriculture 95, 82-91. https://doi.org/10.1016/j.compag.2013.03.009
    Liu, X., Zhang, C., 2018. Fish trajectory tracking based on embedded image processing system. Jiangsu Agricultural Sciences 46(10), 203-207 (in Chinese). https://doi.org/10.15889/j.issn.1002-1302.2018.10.052
    Maradona, A., Marshall, G., Mehrvar, M., Pushchak, R., Laursen, A.E., McCarthy, L.H., Bostan, V., Gilbride, K.A., 2012. Utilization of multiple organisms in proposed early-warning biomonitoring system for real-time detection of contaminants: Preliminary results and modeling. Journal of Hazardous Materials 219-220, 95-102. https://doi.org/10.1016/j.jhazmat.2012.03.064
    Shou, T., Liu, Z., 2017. Approximate algorithm of MTSP on 2D Euclidean space with Delaunay triangulation. Journal of East China University of Science and Technology (Natural Science Edition) 43(6), 895-898 (in Chinese). https://doi.org/10.14135/j.cnki.1006-3080.2017.06.022
    Singh, K.P., Basant, N., Gupta, S., 2011. Support vector machines in water quality management. Analytica Chimica Acta 703(2), 152-162. https://doi.org/10.1016/j.aca.2011.07.027
    Storey, M.V., van der Gaag, B., Burns, B.P., 2011. Advances in on-line drinking water quality monitoring and early warning systems. Water Research 45(2), 741-747. https://doi.org/10.1016/j.watres.2010.08.049
    Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116-132. https://doi.org/10.1109/TSMC.1985.6313399
    Tziakos, I., Cavallaro, A., Xu, L.Q., 2010. Event monitoring via local motion abnormality detection in non-linear subspace. Neurocomputing 73(10), 1881-1891. https://doi.org/10.1016/j.neucom.2009.10.028
    Wang, R., Fu, Z., 2010. Prediction model of dissolved oxygen fuzzy system in aquaculture pond based on neural network. Agriculture Science & Technology 11(8), 14-18 (in Chinese). https://doi.org/10.16175/j.cnki.1009-4229.2010.08.049
    Wu, J., Lu, J., Wang, J., 2009. Application of chaos and fractal models to water quality time series prediction. Environmental Modeling & Software 24(5), 632-636. https://doi.org/10.1016/j.envsoft.2008.10.004
    Yang, X., Jin, W., 2010. GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa. Journal of Environmental Management 91(10), 1943-1951. https://doi.org/10.1016/j.jenvman.2010.04.011
    Zhao, W., 2004. Ecology of Aquaculture Water. China Agricultural Press, Beijing (in Chinese)
    Zhu, X., Zhu, Y., 2019. Problems and countermeasures of controlling cyanobacteria bloom in Taihu Lake. Journal of Environmental Engineering Technology 9(6), 714-719 (in Chinese). https://doi.org/10.12153/j.issn.1674-991X.2019.07.080
    Zhu, Y., Chen, N., 2011. Dynamic forecast of regional groundwater level based on grey Markov chain model. Chinese Journal of Geotechnical Engineering 33(s), 78-82 (in Chinese)
    Zou, S., Yu, Y., 1996. A dynamic factor model for multivariate water quality time series with trends. Journal of Hydrology 178(1), 381-400. https://doi.org/10.1016/0022-1694(95)02787-4
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (35) PDF downloads(0) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint