Design of a water quality monitoring network in a large river system using the genetic algorithm

Park, Su Young; Choi, Jung Hyun; Wang, Sookyun; Park, Seok Soon

doi:10.1016/j.ecolmodel.2006.06.002

Cited by 92 publications

(56 citation statements)

References 14 publications

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“…Park et al [15] proposed application of an optimization method for effective water quality monitoring in a large river system. New optimization algorithms have been developed and some algorithms demonstrated better capability than GA or SCE-UA for some problems [16,17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Jeon

Park

Engel

2014

Water

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Global optimization methods linked with simulation models are widely used for automated calibration and serve as useful tools for searching for cost-effective alternatives for environmental management. A genetic algorithm (GA) and shuffled complex evolution (SCE-UA) algorithm were linked with the Long-Term Hydrologic Impact Assessment (L-THIA) model, which employs the curve number (SCS-CN) method. The performance of the two optimization methods was compared by automatically calibrating L-THIA for monthly runoff from 10 watersheds in Indiana. The selected watershed areas ranged from 32.7 to 5844.1 km 2 . The SCS-CN values and total five-day rainfall for adjustment were optimized, and the objective function used was the Nash-Sutcliffe value (NS value). The GA method rapidly reached the optimal space until the 10th generating population (generation), and after the 10th generation solutions increased dispersion around the optimal space, called a cross hair pattern, because of mutation rate increase. The number of looping executions influenced the performance of model calibration for the SCE-UA and GA method. The GA method performed better for the case of fewer loop executions than the SCE-UA method. For most watersheds, calibration performance using GA was better than for SCE-UA until the 50th generation when the number of model loop executions was OPEN ACCESSWater 2014, 6 3434 around 5150 (one generation has 100 individuals). However, after the 50th generation of the GA method, the SCE-UA method performed better for calibrating monthly runoff compared to the GA method. Optimized SCS-CN values for primary land use types were nearly the same for the two methods, but those for minor land use types and total five-day rainfall for AMC adjustment were somewhat different because those parameters did not significantly influence calculation of the objective function. The GA method is recommended for cases when model simulation takes a long time and the model user does not have sufficient time for an optimization program to search for the best values of calibration parameters. For other cases, the SCE-UA program is recommended for automatic model calibration.

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Section: Introductionmentioning

confidence: 99%

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Jeon

Park

Engel

2014

Water

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“…One of the drivers of pollution events is the recent world population growth that resulted in increasing urbanization and industrialization. Therefore, water pollution and reduction of river flows has become a major threat for the public and environmental health in such a way that the policy makers have called for the design and operation of monitoring networks in river systems to minimize the negative effects of those pollutants (Park et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Design of a water quality monitoring network for the Limpopo River Basin in Mozambique

Chilundo

Kelderman

́keeffe

2008

Physics and Chemistry of the Earth, Parts A/B/C

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The measurement of chemical, physical and biological parameters is important for the characterization of streams health. Thus, cost effective and targeted water quality (WQ) monitoring programmes are required for proper assessment, restoration and protection of such streams. This research proposes a WQ monitoring network for the Limpopo River Basin (LRB) in Mozambique located in Southern Africa, a region prone to severe droughts. In this Basin both anthropogenic and natural driven processes, exacerbated by the increase water demand by the four riparian countries (Botswana, South Africa, Zimbabwe and Mozambique) are responsible for the degradation of surface waters, impairing their downstream use either for aquatic ecosystem, drinking, industrial or irrigation. Hence, physic-chemical, biological and microbiological characteristics at 23 sites within the basin were studied in November-2006 and January-2007. The assessment of the final WQ condition at sampled points was done taking into account the Mozambican guidelines for receiving waters and the environmental WQ standards for effluent discharges together with the WHO guidelines for drinking WQ. The assessed data indicated that sites located at proximities to the border with upstream countries were contaminated with heavy metals. The Elephants subcatchment was found with a relatively better WQ whereas the Changane subcatchment together with the effluent point discharges were found polluted as indicated by the low dissolved oxygen and high total dissolved solids, electric conductivity, total hardness, sodium adsorption ratio and low benthic macroinvertebrates taxa. Significant differences (p<0.05) were found for some parameters when the concentrations recorded in November and January were tested, therefore indicating possible need for monthly monitoring of WQ. From this study it was concluded that a systematic WQ monitoring network composed of 16 stations would fit the conditions of the LRB. Ambient, earl warning, operational and effluents are the main monitoring types recommended. Additional research at a Basin scale was * Corresponding author. Tel.: +258824449340; Fax: +288221492176 e-mail: mchilundo@uem.mz 2 also recommended to identify the major sources, transport and impacts to the downstream ecosystem.

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“…Water quality monitoring networks have traditionally been designed on the basis of experience and intuition (Park et al 2006). However, the selection of monitoring locations should be adjusted as a result of change in the environment.…”

Section: Introductionmentioning

confidence: 99%

Using the VIKOR method to evaluate the design of a water quality monitoring network in a watershed

Chang

Lin

2013

Int. J. Environ. Sci. Technol.

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Water quality monitoring stations play critical roles for analyzing the information of water quality conditions in a watershed. The understanding of water quality conditions of a river system is very important for water resource management. This study applied the VIKOR method, a commonly used method in multiple criteria analysis, to evaluate the design of the water quality monitoring network in the Taipei Water Resource Domain (TWRD). Five criteria were selected for multiple criteria analysis: non-point source pollution, green cover ratio, landslide area ratio, over-utilization area ratio of hillsides, and density of water quality monitoring stations. This study recommended several areas where there is a need for sufficient monitoring records of water quality. The result shows that more monitoring stations are needed in the BeiShih Creek Basin. Although the Nan-Shih Creek Basin covers larger area than the Bei-Shih Creek Basin and the Sin-Dian Creek Basin, it can accept less density of water quality monitoring stations.

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Design of a water quality monitoring network in a large river system using the genetic algorithm

Cited by 92 publications

References 14 publications

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Design of a water quality monitoring network for the Limpopo River Basin in Mozambique

Using the VIKOR method to evaluate the design of a water quality monitoring network in a watershed

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