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Development of an AI Tool to Identify Reference Reaches for Natural Channel Design

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Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 (CSCE 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 250))

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Abstract

The use of reference reaches in the study and analysis of river systems is widespread in the field of geomorphology. We present the development of an artificial intelligence (AI) tool that can be used to identify potential nearby reference reaches based exclusively on river planform geometry. The developed tool implements an AI based approach that uses transfer learning and convolutional neural networks (VGG16 and ResNet50) pre-trained on the ImageNet dataset. The AI tool can be used to identify n potential reference reaches with a distance d of a study site. An implementation of the current development version of the AI tool is presented for a case study on the lower Albany River watershed in northern Ontario, Canada. The outputs of the model are not intended to replace human identification of reference reaches, but rather to provide suggestions for potential reaches that should be considered as reference reaches. The source code for the AI tool is available for download from GitHub at https://github.com/codykupf/river-cnn.

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Acknowledgements

The authors of this study are funded by the Natural Sciences and Engineering Research Council of Canada through the Canada Graduate Scholarship and the Discovery Grant programs. The authors would like to thank the following individuals for their technical input on the machine-learning components of this project: Kristina Kupferschmidt, Graham Taylor, and Terrance Devries.

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Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, Canada

    C. Kupferschmidt & A. Binns

Authors
  1. C. Kupferschmidt
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  2. A. Binns
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Corresponding author

Correspondence to C. Kupferschmidt .

Editor information

Editors and Affiliations

  1. 200 UNIVERSITY AVE W, University of Waterloo, Waterloo, ON, Canada

    Scott Walbridge

  2. Environmental Engineering, Building, Concordia University, Civil, Montreal, QC, Canada

    Mazdak Nik-Bakht

  3. University of Regina, REGINA, SK, Canada

    Kelvin Tsun Wai Ng

  4. Matrix Solutions Calgary, CALGARY, AB, Canada

    Manas Shome

  5. OKANAGAN CAMPUS, University of British Columbia, KELOWNA, BC, Canada

    M. Shahria Alam

  6. THE UNIVERSITY OF WESTERN ONTARIO, ONTARIO, ON, Canada

    Ashraf El Damatty

  7. OKANAGAN CAMPUS, University of British Columbia, KELOWNA, BC, Canada

    Gordon Lovegrove

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Kupferschmidt, C., Binns, A. (2022). Development of an AI Tool to Identify Reference Reaches for Natural Channel Design. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 . CSCE 2021. Lecture Notes in Civil Engineering, vol 250. Springer, Singapore. https://doi.org/10.1007/978-981-19-1065-4_3

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  • DOI: https://doi.org/10.1007/978-981-19-1065-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1064-7

  • Online ISBN: 978-981-19-1065-4

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