| | | Document Type | : | Latin Dissertation | | Language of Document | : | English | | Record Number | : | 53545 | | Doc. No | : | TL23499 | | Call number | : | 1437318 | | Main Entry | : | Lizzette J. Oman | | Title & Author | : | Use of Bayesian belief network to quantify the uncertainty in estimates of salt loading from an irrigated watershedLizzette J. Oman | | College | : | Utah State University | | Date | : | 2006 | | Degree | : | M.S. | | student score | : | 2006 | | Page No | : | 170 | | Abstract | : | Bayesian Belief Networks (BBN) are becoming more widely used in natural resource and water resources applications. The research goal was the development of a BBN model that would estimate salt loading in a river basin based on real-time data. The result is a BBN model for estimating daily, weekly, monthly, and annual salt loading from irrigated watersheds to the San Rafael River Basin (located in the central eastern part of Utah). The model predicts a salt load and quantifies the uncertainty in the loading estimate. Backward and Forward propagation is a tool available in BBNs. Use of them helps to determine which model relationships (e.g., stage-discharge, electrical conductivity-salt concentration) should be improved to reduce the uncertainty in the estimate of salt load. Forward propagation is also useful for water managers in the basin in identifying where investments should be made to improve the collection of real-time and field data. | | Subject | : | Applied sciences; Civil engineering; Agricultural engineering; 0543:Civil engineering; 0539:Agricultural engineering | | Added Entry | : | M. McKee | | Added Entry | : | Utah State University |
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http://lib.clisel.com/site/catalogue/53545
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