Use of Bayesian belief network to quantify the uncertainty in estimates of salt loading from an irri...

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" Use of Bayesian belief network to quantify the uncertainty in estimates of salt loading from an irrigated watershed "
Lizzette J. Oman M. McKee

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 watershed\ Lizzette 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