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Design of a water quality monitoring network in a large river system using the genetic algorithm
- Design of a water quality monitoring network in a large river system using the genetic algorithm
- Park S.-Y.; Choi J.H.; Wang S.; Park S.S.
- Ewha Authors
- 박석순; 최정현
- SCOPUS Author ID
- 박석순; 최정현
- Issue Date
- Journal Title
- Ecological Modelling
- vol. 199, no. 3 SPEC. ISS., pp. 289 - 297
- SCI; SCIE; SCOPUS
- Despite several decades of operations and the increasing importance of water quality monitoring networks, the authorities still rely on experiential insights and subjective judgments in siting water quality monitoring stations. This study proposes an integrated technique which uses a genetic algorithm (GA) and a geographic information system (GIS) for the design of an effective water quality monitoring network in a large river system. In order to develop a design scheme, planning objectives were identified for water quality monitoring networks and corresponding fitness functions were defined using linear combinations of five selection criteria that are critical for developing a monitoring system. The criteria include the representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness levels were obtained through a series of calculations of the fitness functions using GIS data. A sensitivity analysis was performed for major parameters such as the numbers of generations, population sizes and probability of crossover and mutation, in order to determine a good fitness level and convergence for optimum solutions. The proposed methodology was applied to the design of water quality monitoring networks in the Nakdong River system, in Korea. The results showed that only 35 out of 110 stations currently in operation coincide with those in the new network design, therefore indicating that the effectiveness of the current monitoring network should be carefully re-examined. From this study, it was concluded that the proposed methodology could be a useful decision support tool for the optimized design of water quality monitoring networks. © 2006 Elsevier B.V. All rights reserved.
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