Gaussian Process Regression to Predict Incipient Motion of Alluvial Channel

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Gaussian process regression Incipient motion Neural network Sediment transport Soft computing 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Civil EngineeringIITGGuwahatiIndia

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