Using Relevance Vector Machines Approach for Prediction of Total Suspended Solids and Turbidity to Sustain Water Quality and Wildlife in Mud Lake

Mud Lake is a wildlife refuge located in southeastern Idaho just north of Bear Lake that traps sediment from Bear River water flowing into Bear Lake.Very few water quality and sediment observations, if any, exist spatially in Mud Lake. Spatial patterns of sediment deposition may affect Mud Lake flows and habitat; prediction of those patterns should help refuge managers predict water quality constituents and spatial distribution of fine sediment.This will help sustain the purposes of Mud Lake as a habitat and migratory station for species. The main objective of the research is the development of Multivariate Relevant Vector Machine (MVRVM) to predict suspended fine sediment and water quality constituents, and to provide an understanding for the practical problem of determining the amount of data required for the MVRVM. MVRVM isa statistical learning algorithm that is based on Bayes theory.It has been widely used to predict patterns in hydrological systems and other fields. This research represents the first known attempt to use a MVRVM approach to predict transport of very fine sediment andwater quality constituents in a complex natural system. The results demonstrate the ability of the MVRVM to capture and predict the underlying patterns in data.Also careful construction of the experimental design for data collection can lead the Relevant Vectors (RVs is a subset of training observation which carries significant information that is used for prediction) to show locations of significant patterns. The predictions of water quality constituents will be of potential value to US Fish and Wildlife refuge managers in making decisions for operation and management in the case of Mud Lake based on their objectives, and will lead the way for scientists to expand the use of the MVRVM for modeling of suspended fine sediment and water quality in complex natural systems