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Proceedings Paper

Urban drain layout optimization using PBIL algorithm
Author(s): Shanshan Wan; Ying Hao; Dongwei Qiu; Xu Zhao
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Paper Abstract

Strengthen the environmental protection is one of the basic national policies in China. The optimization of urban drain layout plays an important role to the protection of water ecosystem and urban environment. The paper puts forward a method to properly locate urban drain using population based incremental learning (PBIL) algorithm. The main factors such as regional containing sewage capacity, sewage disposal capacity quantity limit of drains within specific area are considered as constraint conditions. Analytic hierarchy process is used to obtain weight of each factor, and spatial analysis of environmental influencing factors is carried on Based on GIS. Penalty function method is put forward to model the problem and object function is to guarantee economy benefit. The algorithm is applied to the drain layout engineering of Nansha District, Guangzhou City, China. The drain layout obtained though PBIL algorithm excels traditional method and it can protect the urban environment more efficiently and ensure the healthy development of water ecosystem more successfully. The result has also proved that PBIL algorithm is a good method in solving this question because of its robust performance and stability which supplied strong technologic support to the sustainable development of environment.

Paper Details

Date Published: 5 November 2008
PDF: 8 pages
Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71442M (5 November 2008); doi: 10.1117/12.812843
Show Author Affiliations
Shanshan Wan, Beijing Univ. of Civil Engineering and Architecture (China)
Ying Hao, Beijing Univ. of Civil Engineering and Architecture (China)
Dongwei Qiu, Beijing Univ. of Civil Engineering and Architecture (China)
Xu Zhao, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 7144:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Xinhao Wang, Editor(s)

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