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Prediction of fecal coliform counts ...

Colomb, Matthias A.

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Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model./
Author:	Colomb, Matthias A.
Description:	94 p.
Notes:	Source: Masters Abstracts International, Volume: 45-04, page: 2026.
Contained By:	Masters Abstracts International45-04.
Subject:	Engineering, Chemical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441468

Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model.
Colomb, Matthias A.

Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model. - 94 p.

Source: Masters Abstracts International, Volume: 45-04, page: 2026.

Thesis (M.S.)--University of South Alabama, 2007.

Fecal coliform bacteria (FCB) are indicative of the presence of organisms from the intestinal tract of humans and other animals. Oysters grown and harvested in waters with high concentration of FCB may accumulate pathogens in their tissues responsible for the transmission of waterborne diseases. As such, an accurate assessment of FCB count is critical for healthy practices of oyster harvesting. A method was developed for accurately predicting the FCB count for use in oyster harvesting in Alabama coastal waters. The method utilizes Bayesian statistics and dynamic time series modeling with salinity and local rainfall as regressors. Data from 1994 to the present were used to develop the models. Data were classified based on sampling stations, seasons, and on FCB response to the regressors. Such data were combined in a judicious manner and used in the development of the ALOHA BAM prior. A likelihood model that accounted for local sewage spills was combined with the ALOHA BAM prior for a final posterior prediction. The proposed approach outperforms the conventionally adopted mechanism for assessing FCB count for oyster harvesting. Future work would likely result in a proposal for an alternative oyster harvesting management plan which could include a systematic random sampling strategy.Subjects--Topical Terms:

1018531
Engineering, Chemical.

Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model.
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100 1 $a Colomb, Matthias A. $3 1923335
245 1 0 $a Prediction of fecal coliform counts in Mobile Bay, Alabama using a Bayesian model.
300 $a 94 p.
500 $a Source: Masters Abstracts International, Volume: 45-04, page: 2026.
500 $a Adviser: Manish Misra.
502 $a Thesis (M.S.)--University of South Alabama, 2007.
520 $a Fecal coliform bacteria (FCB) are indicative of the presence of organisms from the intestinal tract of humans and other animals. Oysters grown and harvested in waters with high concentration of FCB may accumulate pathogens in their tissues responsible for the transmission of waterborne diseases. As such, an accurate assessment of FCB count is critical for healthy practices of oyster harvesting. A method was developed for accurately predicting the FCB count for use in oyster harvesting in Alabama coastal waters. The method utilizes Bayesian statistics and dynamic time series modeling with salinity and local rainfall as regressors. Data from 1994 to the present were used to develop the models. Data were classified based on sampling stations, seasons, and on FCB response to the regressors. Such data were combined in a judicious manner and used in the development of the ALOHA BAM prior. A likelihood model that accounted for local sewage spills was combined with the ALOHA BAM prior for a final posterior prediction. The proposed approach outperforms the conventionally adopted mechanism for assessing FCB count for oyster harvesting. Future work would likely result in a proposal for an alternative oyster harvesting management plan which could include a systematic random sampling strategy.
590 $a School code: 0491.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Engineering, Marine and Ocean. $3 1019064
650 4 $a Environmental Sciences. $3 676987
650 4 $a Engineering, Environmental. $3 783782
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710 2 0 $a University of South Alabama. $3 1017878
773 0 $t Masters Abstracts International $g 45-04.
790 1 0 $a Misra, Manish, $e advisor
790 $a 0491
791 $a M.S.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1441468