Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yorke, T.H., Oberg, K.A.: Measuring river velocity and discharge with acoustic Doppler profilers. Flow Meas. Instrum. 13, 191–195 (2002)
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)
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)
Dijkgraaf, S.: The functioning and significance of the lateral-line organs. Biol. Rev. 38(1), 51–105 (1963)
Nestler, J. M., Pickens, J. L., Evans, J., Haskins, R. W.: Multiple sensor fish surrogate for acoustic and hydraulic data collection. US5517465 A (1996)
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)
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)
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)
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)
Shizhe, T.: Underwater artificial lateral line flow sensors. Microsyst. Technol. 20, 2123–2136 (2014)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
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)
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)
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)
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)
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)
Bundesministerium für Land- und Forstwirt-schaft, Umwelt und Wasserwirtschaft (Hrsg.): Leitfaden zum Bau von Fischaufstiegshilfen. Wien (2012)
Liaw, A., Wiener, M.: Classification and regression by random forest. R News 2(3), 18–22 (2002)
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2015). http://www.R-project.org/
Nash, J.E., Sutcliffe, J.V.: River flow forecasting through conceptual models. part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
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)
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)
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)
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)
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)
Acknowledgement
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Fukuda, S., Tuhtan, J.A., Fuentes-Perez, J.F., Schletterer, M., Kruusmaa, M. (2016). Random Forests Hydrodynamic Flow Classification in a Vertical Slot Fishway Using a Bioinspired Artificial Lateral Line Probe. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9835. Springer, Cham. https://doi.org/10.1007/978-3-319-43518-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-43518-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43517-6
Online ISBN: 978-3-319-43518-3
eBook Packages: Computer ScienceComputer Science (R0)