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A probabilistic expert systems appro...

Ghabayen, Said M. S.

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A probabilistic expert systems approach for analysis and optimization of a large-scale water resources system.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A probabilistic expert systems approach for analysis and optimization of a large-scale water resources system./
作者:	Ghabayen, Said M. S.
面頁冊數:	138 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0900.
Contained By:	Dissertation Abstracts International65-02B.
標題:	Engineering, Civil. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3122880
ISBN:	0496701943

A probabilistic expert systems approach for analysis and optimization of a large-scale water resources system.
Ghabayen, Said M. S.

A probabilistic expert systems approach for analysis and optimization of a large-scale water resources system. - 138 p.

Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0900.

Thesis (Ph.D.)--Utah State University, 2004.

Groundwater aquifer is the only natural source of fresh water for the Gaza Strip. However, it is severely polluted and overused. Providing adequate water supply and sanitation services for the rapidly growing population within the limits of available physical, institutional, and operational capacities has been nearly impossible. A plan is being instituted that will expand the water supply and wastewater infrastructure of the Gaza Strip into an integrated system, including operational policies to provide for recovery of the aquifer. Components of this plan are subject to a great deal of uncertainty, and understanding these uncertainties is a key to the success of the plan.

ISBN: 0496701943Subjects--Topical Terms:

783781
Engineering, Civil.

A probabilistic expert systems approach for analysis and optimization of a large-scale water resources system.
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520 $a Groundwater aquifer is the only natural source of fresh water for the Gaza Strip. However, it is severely polluted and overused. Providing adequate water supply and sanitation services for the rapidly growing population within the limits of available physical, institutional, and operational capacities has been nearly impossible. A plan is being instituted that will expand the water supply and wastewater infrastructure of the Gaza Strip into an integrated system, including operational policies to provide for recovery of the aquifer. Components of this plan are subject to a great deal of uncertainty, and understanding these uncertainties is a key to the success of the plan.
520 $a A Bayesian belief network (BBN) simulation model is presented as a tool for characterization of uncertainties in the operation and management of the integrated Gaza Strip water resources system. This model represents a new application of probabilistic modeling tools to a large-scale, complex, integrated water resources system. The BBN model also serves as a multi-objective optimization framework to evaluate alternative policies that will have an effect on conflicting social and management goals. The optimization objectives are (1) minimize the capital and O&M supply cost for water development alternatives; (2) minimize the uncertainty in the different system output variables; (3) maximize system reliability; and (4) find the optimal tariff formula to satisfy system efficiency and cost recovery requirements.
520 $a A BBN approach could serve as an interactive planning tool to achieve the Gaza water system planning objectives by incorporating different sources of information including existing data, new data, expert judgment, and stakeholder opinion. This study has demonstrated the strength of BBNs in improving the information content of available water resources data. In this context, the BBN has served as an integrated analytical framework to weld different pieces of information together and optimize their use. It also identified areas where additional data are required to reduce uncertainty in management decisions.
520 $a The main outcome of the application of the BBN model includes operational polices that maximize cost recovery, economic efficiency, demand satisfaction, and water quality satisfaction. Based on these, optimal prices were determined. The model also identified key areas of uncertainty and key areas of management to minimize this uncertainty. Finally, the model outlined policies to cope with system emergency conditions, such as severe drought, major facility failure, and energy failure.
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