Random Forests Hydrodynamic Flow Classification in a Vertical Slot Fishway Using a Bioinspired Artificial Lateral Line Probe

  • Shinji FukudaEmail author
  • Jeffrey A. Tuhtan
  • Juan Francisco Fuentes-Perez
  • Martin Schletterer
  • Maarja Kruusmaa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9835)


Ecohydraulic studies rely on observations of fish behavior with hydrodynamic measurements. Most commonly, observed fish locations are compared with maps of the bulk flow velocity and depth. Fish use their lateral line to sense hydrodynamic interactions mediated by body-oriented spatial gradients. To improve studies on fish an artificial lateral line probe (LLP) is tested on its ability to classify either the “slot” or “pool” regions within 28 basins of a vertical slot fishway. Random forests classification is applied using four models based on high-frequency (200 Hz) recordings using 11 collocated pressure sensors and two triaxial accelerometers. It was found that the assigned classification task proved to be reliable, with 100 % correct classification of all four models, across all 28 basins. Preliminary results from the first field study of this new sensing platform show the LLP-random forests workflow can provide robust, highly accurate classification of turbulent flows experienced by fish innatura.


Random forests Lateral line probe Vertical slot fishway Turbulent flow Hydrodynamic classification 



This work was supported in part by the Grant-in-aid for Young Scientists A (25712026) and Grant-in-aid for Challenging Exploratory Research (26660190) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The research leading to these results has received funding from BONUS, the joint Baltic research and development programme, co‐financed by the European Union’s Seventh Framework Programme (2007–2013) under the BONUS Implementation Agreement. National funding for this work has been provided by the German Federal Ministry for Education and Research (BMBF FKZ:03F0687A) and the Estonian Environmental Investment Centre (KIK P.7254 C.3255). The work has also been partly financed by the EU FP7 project ROBOCADEMY (No. 608096) and the TIWAG research project “Robofish”, Austria.


  1. 1.
    Yorke, T.H., Oberg, K.A.: Measuring river velocity and discharge with acoustic Doppler profilers. Flow Meas. Instrum. 13, 191–195 (2002)CrossRefGoogle Scholar
  2. 2.
    Kalmijn, A.J.: Hydrodynamic and acoustic field detection. In: Atema, J., Fay, R.R., Popper, A.N., Tavolga, W.N. (eds.) Sensory Biology of Aquatic Animals, pp. 83–130. Springer, New York (1988)CrossRefGoogle Scholar
  3. 3.
    Tuhtan, J.A., Fuentes-Pérez, J.F., Strokina, N., Toming, G., Musall, M., Noack, M., Kämäräinen, J.K., Kruusmaa, M.: Design and application of a fish-shaped lateral line probe for flow measurement. Rev. Sci. Instrum. 87(4), 45110 (2016)CrossRefGoogle Scholar
  4. 4.
    Dijkgraaf, S.: The functioning and significance of the lateral-line organs. Biol. Rev. 38(1), 51–105 (1963)CrossRefGoogle Scholar
  5. 5.
    Nestler, J. M., Pickens, J. L., Evans, J., Haskins, R. W.: Multiple sensor fish surrogate for acoustic and hydraulic data collection. US5517465 A (1996)Google Scholar
  6. 6.
    Deng, Z.D., Lu, J., Myjak, M.J., Martinez, J.J., Tian, C., Morris, S.J., Carlson, T.J., Zhou, D., Hou, H.: Design and implementation of a new autonomous sensor fish to support advanced hydropower development. Rev. Sci. Instrum. 85(11), 115001 (2014)CrossRefGoogle Scholar
  7. 7.
    Yang, Y., Chen, J., Engel, J., Pandya, S., Chen, N., Tucker, C., Coombs, S., Jones, D.L., Liu, C.: Distant touch hydrodynamic imaging with an artificial lateral line. Proc. Natl. Acad. Sci. 103(50), 18891–18895 (2006)CrossRefGoogle Scholar
  8. 8.
    Klein, A., Bleckmann, H.: Determination of object position, vortex shed-ding frequency and flow velocity using artificial lateral line canals. Beilstein J. Nanotechnol. 2, 276–283 (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, W., Xie, G.: Online high-precision probabilistic localization of robotic fish using visual and inertial cues. IEEE Trans. Ind. Electron. 62(2), 1113–1124 (2015)CrossRefGoogle Scholar
  10. 10.
    Shizhe, T.: Underwater artificial lateral line flow sensors. Microsyst. Technol. 20, 2123–2136 (2014)CrossRefGoogle Scholar
  11. 11.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Cutler, R.D., Edwards, T.C., Beard, K.H., Cutler, K.T., Gibson, H.J., Lawler, J.J.: Random forests for classification in ecology. Ecology 88, 2783–2792 (2007)CrossRefGoogle Scholar
  13. 13.
    Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., Mouton, A.M.: Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ. Model Softw. 47, 1–6 (2013)CrossRefGoogle Scholar
  14. 14.
    Strokina, N., Kämäräinen, J.K., Tuhtan, J.A., Fuentes-Pérez, J.F., Kruusmaa, M.: Joint estimation of bulk flow velocity and angle using a lateral line probe. IEEE Trans. Instrum. Meas. 65(3), 601–613 (2016)CrossRefGoogle Scholar
  15. 15.
    Fuentes-Pérez, J.F., Tuhtan, J.A., Carbonell-Baeza, R., Musall, M., Toming, G., Muhammad, N., Kruusmaa, M.: Current velocity estimation using a lateral line probe. Ecol. Eng. 85, 296–300 (2015)CrossRefGoogle Scholar
  16. 16.
    Tuhtan, J.A., Strokina, N., Fuentes-Pérez, J.F., Muhammad, N., Musall, M., Noack, M., Toming, G., Kämäräinen, J.-K., Kruusmaa, M., Schletterer, M.: Ecohydraulic flow sensing and classification using a lateral line probe. In: Proceedings of 11th International Symposium on Ecohydraulics, Melbourne, Australia (2016)Google Scholar
  17. 17.
    Bundesministerium für Land- und Forstwirt-schaft, Umwelt und Wasserwirtschaft (Hrsg.): Leitfaden zum Bau von Fischaufstiegshilfen. Wien (2012)Google Scholar
  18. 18.
    Liaw, A., Wiener, M.: Classification and regression by random forest. R News 2(3), 18–22 (2002)Google Scholar
  19. 19.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2015).
  20. 20.
    Nash, J.E., Sutcliffe, J.V.: River flow forecasting through conceptual models. part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970)CrossRefGoogle Scholar
  21. 21.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., Müller, M.: pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011)CrossRefGoogle Scholar
  23. 23.
    Cea, L., Pena, L., Puertas, J., Vázquez-Cendón, M.E., Peña, E.: Application of several depth-averaged turbulence models to simulate flow in vertical slot fishways. J. Hydraul. Eng. 133, 160–172 (2007)CrossRefGoogle Scholar
  24. 24.
    Fukuda, S., Spreer, W., Yasunaga, E., Yuge, K., Sardsud, V., Muller, J.: Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agric. Water Manage. 116, 142–150 (2013)CrossRefGoogle Scholar
  25. 25.
    Fukuda, S., Yasunaga, E., Nagle, M., Yuge, K., Sardsud, V., Spreer, W., Muller, J.: Modelling the relationship between peel colour and the quality of fresh mango fruit using random forests. J. Food Eng. 131, 7–17 (2014)CrossRefGoogle Scholar
  26. 26.
    Tuhtan, A., Strokina, N., Toming, G., Muhammad, N., Kruusmaa, M., Kämäräinen J.: Hydrodynamic classification of natural flows using an artificial lateral line and frequency domain feature. In: 36th IAHR World Congress (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shinji Fukuda
    • 1
    Email author
  • Jeffrey A. Tuhtan
    • 2
    • 3
  • Juan Francisco Fuentes-Perez
    • 3
  • Martin Schletterer
    • 4
  • Maarja Kruusmaa
    • 3
  1. 1.Tokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.SJE Ecohydraulic Engineering GmbHStuttgartGermany
  3. 3.Centre for BioroboticsTallinnEstonia
  4. 4.TIWAG - Tiroler Wasserkraft AGInnsbruckAustria

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