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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)

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.

Keywords

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

Notes

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.

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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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