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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical analysis of regional geographic information systems (GIS) data to predict water quality in streams./
作者:	Horsburgh, Jeffrey Scott.
面頁冊數:	132 p.
附註:	Major Professor: David K. Stevens.
Contained By:	Masters Abstracts International39-04.
標題:	Biology, Limnology. -
電子資源:	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.
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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.
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