Optimized selection of river sampling sites

Dixon, W. V.; Smyth, Gordon K.; Chiswell, B.

doi:10.1016/s0043-1354(98)00289-9

Cited by 41 publications

(38 citation statements)

References 5 publications

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“…As solution for that considering the geospatial data such as catchment area, urbanization factors and pollutant loading are suggested for criteria of optimization algorithm known as simulated annealing [11,12]. The other solution is enumerate the all possible monitoring locations.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Optimized Selection Methods of Sampling Sites Network for Water Quality Monitoring in a River

Liyanage

Marasinghe

Yamada

2016

IJAE

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Abstract:Design of water quality monitoring network in a river basin is most necessary when the cost concerns of the process is considered. The most of the methods have followed a set of objectives in the last few decades to find optimized selection of sampling locations. One of the main contributions of this paper is to design an optimized selection of sampling sites network considering the newly identified monitoring sites rather than the points which are available in the current network that exists. This study used four criteria including one urbanization factor which is in the development pressure index (DPI) rather than the factors environmental pressure index (EPI) to evaluate the objectives. Multi objective analysis method and genetic algorithm were applied for find optimal network and three constraints are used to obtain a practical solution. The other main purpose of this study is to compare the efficiency between method of genetic algorithm and the brute-force approach by considering the computation time of fitness functions. Further, the fitness function was defined using the linear combination of the criteria. Both proposed that the optimal water quality monitoring networks are reasonably sufficient to enhance the existing network in Kelani River. The genetic algorithm has a high performance to find the fitness functions even though all possible combinations of monitoring sites are identified by brute-force approach.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Optimized Selection Methods of Sampling Sites Network for Water Quality Monitoring in a River

Liyanage

Marasinghe

Yamada

2016

IJAE

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“…In the field of hydrology, SA was used to optimize a rainfall gaging network with the objective of providing estimates of mean precipitation while minimizing the estimation variance [59]. Dixon et al [13] also used SA to optimize a suite of river sampling sites on the Logan River and the Albert River in Queensland, Australia. Several more integrated simulation and optimization approaches were developed to identify the optimal locations in response to ground water, surface water, and air quality monitoring requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Chang

Makkeasorn

2009

Environ Model Assess

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Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are all intimately related with each other to form water balance dynamics on the surface of the Earth. To monitor change in hydrological systems with minimum effort, however, hydrological monitoring networks at the watershed scale should be deployed at critical locations to advance the monitoring and sensing capability. One of the science questions is how to develop an optimum arrangement/ distribution strategy of those monitoring platforms with respect to hydrological components subject to technical and resources constraints. While the complexities arise from the integration of highly heterogeneous data streams in the hydrological cycle under uncertainty, there is an acute need to develop a site screening and sequencing procedure permitting a cost-effective search for final site selection. This paper purports to develop such an approach to address the optimal site selection strategy by integrating satellite remote sensing images with a grey integer programming (GIP) model. The approach uses spatial information on the range of likely values temporally encountered for a number of biophysical descriptors in support of the optimization analysis under uncertainty. Practical implementation was assessed by a case study in a semi-arid watershed-the Choke Canyon Reservoir watershed, south Texas. GIS-based GIP modeling technique successfully supports the screening and sequencing mechanism based on the composite satellite images, which smoothly prioritizes the relative importance and provides the rank order scores across all candidate sites. With the aid of such a synergistic approach, seven locations out of 563 candidate sites were eventually selected and confirmed by a field investigation.

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“…In general, the numbers of sampling sites is limited by the available budget and cannot be widely and densely distributed. Dixon et al (1999) also indicated that the cost involved in the subsequent investigation to find the source of a detected pollution event should be considered in determining proper sampling sites. Sanders et al (1983) pointed that location is the most critical factor in designing a sampling network.…”

Section: Introductionmentioning

confidence: 99%

“…Although some other researches had applied optimization models to determine the locations of sampling sites, the identification capability to locate the source of a detected pollution event is still not considered (Strobl et al 2006;Icaga 2005;Ning and Chang 2002, 2004, 2005. As described by Dixon et al (1999), the method proposed by Sharp (1971) may not be capable of locating the optimal placement of sampling sites. Dixon et al (1999) thus proposed a siting method based on the simulated annealing (SA) algorithm, graph theory, and a geographical information system.…”

Section: Introductionmentioning

confidence: 99%

“…As described by Dixon et al (1999), the method proposed by Sharp (1971) may not be capable of locating the optimal placement of sampling sites. Dixon et al (1999) thus proposed a siting method based on the simulated annealing (SA) algorithm, graph theory, and a geographical information system. The cost function of their optimization model is formulated based on the expected cost for obtaining the information subsequent to detecting the water quality problem at a sampling site.…”

Section: Introductionmentioning

confidence: 99%

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Siting analyses for water quality sampling in a catchment

Kao

Lin

et al. 2007

Environ Monit Assess

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Pollution loads discharged from upstream development or human activities significantly degrade the water quality of a reservoir. The design of an appropriate water quality sampling network is therefore important for detecting potential pollution events and monitoring pollution trends. However, under a limited budgetary constraint, how to site an appropriate number of sampling stations is a challenging task. A previous study proposed a method applying the simulated annealing algorithm to design the sampling network based on three cost factors including the number of reaches, bank length, and subcatchment area. However, these factors are not directly related to the distribution of possible pollution. Thus, this study modified the method by considering three additional factors, i.e. total phosphorus, nitrogen, and sediment loads. The larger the possible load, the higher the probability of a pollution event may occur. The study area was the Derchi reservoir catchment in Taiwan. Pollution loads were derived from the AGNPS model with rainfall intensity estimated using the Thiessen method. Analyses for a network with various numbers of sampling sites were implemented. The results obtained based on varied cost factors were compared and discussed. With the three additional factors, the chosen sampling network is expected to properly detect pollution events and monitor pollution distribution and temporal trends.

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Optimized selection of river sampling sites

Cited by 41 publications

References 5 publications

Comparison of Optimized Selection Methods of Sampling Sites Network for Water Quality Monitoring in a River

Comparison of Optimized Selection Methods of Sampling Sites Network for Water Quality Monitoring in a River

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Siting analyses for water quality sampling in a catchment

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