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

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• 关键词：Random forests ; Lateral line probe ; Vertical slot fishway ; Turbulent flow ; Hydrodynamic classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9835
• 期：1
• 页码：297-307
• 全文大小：2,364 KB
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• 作者单位：Shinji Fukuda (17)
  Jeffrey A. Tuhtan (18) (19)
  Juan Francisco Fuentes-Perez (19)
  Martin Schletterer (20)
  Maarja Kruusmaa (19)
  
  17. Tokyo University of Agriculture and Technology, Tokyo, Japan
  18. SJE Ecohydraulic Engineering GmbH, Stuttgart, Germany
  19. Centre for Biorobotics, Tallinn, Estonia
  20. TIWAG - Tiroler Wasserkraft AG, Innsbruck, Austria
  
• 丛书名：Intelligent Robotics and Applications
• ISBN：978-3-319-43518-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9835

文摘

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.