Grey fuzzy optimization model for water quality management of a river system

Karmakar, Subhankar; Mujumdar, P. P.

doi:10.1016/j.advwatres.2006.04.003

Cited by 139 publications

(70 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Together with previous studies, his results imply that even a small increase in the impervious surface in a watershed might have significant impacts on the chemical characteristics of water and the biota of streams. This nonlinearity may be derived from the random nature of the hydrodynamic conditions of river systems, meteorological processes, and a shortage of available monitoring data [15,17,18]. It is also noteworthy that there was only one breakpoint (i.e., threshold) in the relationships between land uses and water quality indicators, regardless of the different thresholds reported in previous studies.…”

Section: Introductionmentioning

confidence: 88%

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Hwang

Park

et al. 2016

Water

View full text Add to dashboard Cite

Abstract:Although close relationships between the water quality of streams and the types of land use within their watersheds have been well-documented in previous studies, many aspects of these relationships remain unclear. We examined the relationships between urban land use and water quality using data collected from 527 sample points in five major rivers in Korea-the Han, Geum, Nakdong, Younsan, and Seomjin Rivers. Water quality data were derived from samples collected and analyzed under the guidelines of the Korean National Aquatic Ecological Monitoring Program, and land use was quantified using products provided by the Korean Ministry of the Environment, which were used to create a Geographic Information System. Linear models (LMs) and generalized additive models were developed to describe the relationships between urban land use and stream water quality, including biological oxygen demand (BOD), total nitrogen (TN), and total phosphorous (TP). A comparison between LMs and non-linear models (in terms of R 2 and Akaike's information criterion values) indicated that the general additive models had a better fit and suggested a non-linear relationship between urban land use and water quality. Non-linear models for BOD, TN, and TP showed that each parameter had a similar relationship with urban land use, which had two breakpoints. The non-linear models suggested that the relationships between urban land use and water quality could be categorized into three regions, based on the proportion of urban land use. In moderate urban land use conditions, negative impacts of urban land use on water quality were observed, which confirmed the findings of previous studies. However, the relationships were different in very low urbanization or very high urbanization conditions. Our results could be used to develop strategies for more efficient stream restoration and management, which would enhance water quality based on the degree of urbanization in watersheds. In particular, land use management for enhancing stream water quality might be more effective when urban land use is in the range of 1.1%-31.5% of a watershed. If urban land use exceeds 31.5% in a watershed, a more comprehensive approach would be required because water quality would not respond as rapidly as expected.

show abstract

Section: Introductionmentioning

confidence: 88%

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Hwang

Park

et al. 2016

Water

View full text Add to dashboard Cite

show abstract

“…Under many situations, the values of the quantitative and qualitative criteria are often imprecise or vague, therefore GRA, one of the sub-branches of Deng's Grey Theory [5] which has been applied in prediction, control, social and economic system management, decision making about environmental systems in recent years [6][7][8][9], is becoming a handy approach, like Zhen-qiang et al [10] which presented an analysis for the facility's location of logistics distribution network. Huang and Huang [11] integrated fuzzy and grey modeling methods for predicting the monthly average temperatures in Taipei.…”

Section: Literature Reviewmentioning

confidence: 99%

Hiyerarşik gri ilişkisel analiz yöntemiyle çok kriterli çağri merkezi yeri seçimi

Birgün

Güngör

2014

J Aeronaut Space Technol

View full text Add to dashboard Cite

Facility location selection is a decision problem that takes into consideration both qualitative and quantitative factors and also is one of the most important strategic decisions affecting organizations in terms of business success. Fundamentally, it comprises evaluating a group of alternative sites on the basis of multiple criteria. In this context when alternatives and factors are multitudinous, multi-criteria decision making methods are being used for successful decisions. In this paper the decision problem is exemplified by applying on a part of a project for a call center site selection by using one of the multi-criteria decision-making methods, namely hierarchy grey relational analysis, based on application of analytic hierarchy process (AHP) and grey relational analysis (GRA) methods. The goal of the selection is to determine the most appropriate location among the alternatives.Nine cities given as alternatives are evaluated and compared against human resources, economic and regional conditions including fourteen sub-criteria.

show abstract

“…A grey number (Deng 1982(Deng , 1987Huang et al 1995;Karmakar & Mujumdar 2006) is defined as a number whose exact value is unknown, but which falls within an interval that is known. A grey number thus enables us to represent the uncertainty associated with a given parameter by means of an interval whose upper and lower limits are known, although we have no information about the parameter's distribution within the interval itself (Liu & Lin 1998).…”

Section: Grey Numbersmentioning

confidence: 99%

“…With this technique, uncertainty/ imprecision is characterized simply by defining one interval and, in this respect, grey numbers can be considered like a fuzzy number confined to only one 'membership function' level (Karmakar & Mujumdar 2006), even though their conceptual background differs (Huang et al 1995). As in the case of fuzzy numbers, it is possible to employ specific mathematics or 'grey mathematics' (Wang & Wu 1998) in order to arrive at a definition of the grey numbers representing the output variables.…”

Section: Introductionmentioning

confidence: 99%

Pipe roughness calibration in water distribution systems using grey numbers

Alvisi

Franchini

2010

Journal of Hydroinformatics

View full text Add to dashboard Cite

This paper presents a procedure based on the use of grey numbers for the calibration (with uncertainty) of pipe roughness in water distribution systems. The pipe roughness uncertainty is represented through the grey number amplitude (or interval). The procedure is of a wholly general nature and can be applied for the calibration (with uncertainty) of other parameters or quantities, such as nodal demands. In this paper, for the purpose of roughness calibration, a certain number of nodal head measurements made under different demand conditions is assumed to be available at different locations (nodes); all other topological and geometric characteristics of the system are considered to be known exactly.The general approach to pipe roughness calibration (taking account of uncertainty) focuses on identifying the grey roughness values which produce grey head values at the measuring nodes such as to encompass the observed values grouped on the basis of the different demand scenarios and, at the same time, have as small an 'amplitude' as possible.The proposed procedure was applied to two synthetic case studies and to one real network.The tests on the synthetic case studies show that the proposed procedure is able to correctly solve the inverse problem, i.e. it can identify the known grey roughness numbers even when they overlap; the same applies when the known grey roughness numbers collapse into known white roughness numbers. The test on the real case offers the possibility of highlighting the potentials of the procedure when applied within a context where measurement errors and other uncertainties are present. The procedure entails computing times that may become lengthy. However, it is possible to reduce these computing times considerably by replacing the hydraulic simulator-to which a number of calls must be made during the calibration procedure (for objective function evaluation)-with an approximation based on a first-order Taylor series expansion. This approach introduces acceptable approximations within the context of the problem considered.

show abstract

Grey fuzzy optimization model for water quality management of a river system

Cited by 139 publications

References 31 publications

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models

Hiyerarşik gri ilişkisel analiz yöntemiyle çok kriterli çağri merkezi yeri seçimi

Pipe roughness calibration in water distribution systems using grey numbers

Contact Info

Product

Resources

About