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Statistical analysis of regional geo...

Horsburgh, Jeffrey Scott.

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Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams./
Author:	Horsburgh, Jeffrey Scott.
Description:	132 p.
Notes:	Major Professor: David K. Stevens.
Contained By:	Masters Abstracts International39-04.
Subject:	Biology, Limnology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1403208
ISBN:	0493117547

Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams.
Horsburgh, Jeffrey Scott.

Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams. - 132 p.

Major Professor: David K. Stevens.

Thesis (M.S.)--Utah State University, 2001.

The objective of this research was to develop a statistical methodology for predicting water quality in data-poor watersheds using geographic information systems (GIS) data coverages and Bayesian networks. The K-nearest neighbor Bayesian network (KNNBN) methodology is described, including the generation of a preliminary regional database of potential predictors and water quality data for 141 watersheds in southern Idaho. Its effectiveness is demonstrated through the generation of regional Bayesian networks for estimating total phosphorous, total nitrate, and total suspended solids. The predictive skill of the resulting Bayesian networks is demonstrated in a validation set and in a small case study in the upper Teton watershed in southern Idaho.

ISBN: 0493117547Subjects--Topical Terms:

1018638
Biology, Limnology.

Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams.
LDR:01693nam 2200301 a 45 001 936414
005 20110510
008 110510s2001 eng d
020 $a 0493117547
035 $a (UnM)AAI1403208
035 $a AAI1403208
040 $a UnM $c UnM
100 1 $a Horsburgh, Jeffrey Scott. $3 1260104
245 1 0 $a Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams.
300 $a 132 p.
500 $a Major Professor: David K. Stevens.
500 $a Source: Masters Abstracts International, Volume: 39-04, page: 1224.
502 $a Thesis (M.S.)--Utah State University, 2001.
520 $a The objective of this research was to develop a statistical methodology for predicting water quality in data-poor watersheds using geographic information systems (GIS) data coverages and Bayesian networks. The K-nearest neighbor Bayesian network (KNNBN) methodology is described, including the generation of a preliminary regional database of potential predictors and water quality data for 141 watersheds in southern Idaho. Its effectiveness is demonstrated through the generation of regional Bayesian networks for estimating total phosphorous, total nitrate, and total suspended solids. The predictive skill of the resulting Bayesian networks is demonstrated in a validation set and in a small case study in the upper Teton watershed in southern Idaho.
590 $a School code: 0241.
650 4 $a Biology, Limnology. $3 1018638
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Environmental Sciences. $3 676987
650 4 $a Physical Geography. $3 893400
690 $a 0368
690 $a 0768
690 $a 0775
690 $a 0793
710 2 0 $a Utah State University. $3 960049
773 0 $t Masters Abstracts International $g 39-04.
790 $a 0241
790 1 0 $a Stevens, David K., $e advisor
791 $a M.S.
792 $a 2001
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1403208