Application of Relevance Vector Machine for Sediment Transport Estimation

Estimation of sediment concentrations in streams and rivers is important for water resources management and projects. Sediment concentration is generally determined from direct measurements, or estimated from sediment transport equations that require detailed information about the flow and sediment characteristics. However, there is often a large discrepancy between these models and observations. The complexity of sediment transport processes presents an opportunity for the application of alternate methods. As a fairly recent computing tool, relevance vector machines (RVMs) are gaining popularity in the fields of machine learning and pattern recognition. The objective of this study is to develop an RVM approach for estimation of sediment concentrations. The resulting RVM model is then trained and tested on a large data set and the performance of the RVM approach is compared with more conventional transport formulae.