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Gaussian Process Regression to Predict Incipient Motion of Alluvial Channel

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Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 336))

Abstract

Incipient motion of alluvial channel flow, which relates the beginning of sediment movement, has been extensively studied in the past few decades, and many equations have been developed which essentially differ from each other in derivation and form. As the process is extremely complex, getting deterministic or analytical forms of process phenomena is too difficult. Gaussian process regression (GPR), which is particularly useful in modeling processes about which adequate knowledge of the physics is limited, is presented here as a complementary tool to model the incipient motion problems. The prediction capability of the model has been found to be satisfactory.

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Authors and Affiliations

  1. Department of Civil Engineering, IITG, Guwahati, India

    Jaideep Sehrawat, Mahesh Patel & Bimlesh Kumar

Authors
  1. Jaideep Sehrawat
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  2. Mahesh Patel
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  3. Bimlesh Kumar
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Corresponding author

Correspondence to Jaideep Sehrawat .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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Sehrawat, J., Patel, M., Kumar, B. (2015). Gaussian Process Regression to Predict Incipient Motion of Alluvial Channel. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_35

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  • DOI: https://doi.org/10.1007/978-81-322-2220-0_35

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

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