Modeling Groundwater Contamination under Uncertainty: A Factorial-Design-Based Stochastic Approach

Qin, Qian

doi:10.3808/jei.200800106

Cited by 25 publications

(13 citation statements)

References 12 publications

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“…Nevertheless, there are a variety of uncertainties involved in modeling efforts (Qin et al 2008). Such uncertainties are mostly derived from factors related to various economic and environmental conditions, and can significantly influence the system performance.…”

Section: Ftspmentioning

confidence: 99%

“…In comparison, factorial analysis was widely used in experiments involving multiple factors where it was necessary to study the joint effects of the factors on a response (Montgomery 2001). The idea of factorial analysis was to arrange the simulations in such a way that the variations in simulation responses were obtained under different combinations (Box et al 1978;Qin et al 2008). Typically, a 2 k factorial design is constituted by k factors, with each at two levels.…”

Section: Ftspmentioning

confidence: 99%

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Factorial two-stage stochastic programming for water resources management

Zhou

Huang

2010

Stoch Environ Res Risk Assess

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This study presents a factorial two-stage stochastic programming (FTSP) approach for supporting water resource management under uncertainty. FTSP is developed through the integration of factorial analysis and two-stage stochastic programming (TSP) methods into a general modeling framework. It can handle uncertainties expressed as probability distributions and interval numbers. This approach has two advantages in comparison to conventional inexact TSP methods. Firstly, FTSP inherits merits of conventional inexact two-stage optimization approaches. Secondly, it can provide detailed effects of uncertain parameters and their interactions on the system performance. The developed FTSP method is applied to a hypothetical case study of water resources systems analysis. The results indicate that significant factors and their interactions can be identified. They can be further analyzed for generating water allocation decision alternatives in municipal, industrial and agricultural sectors. Reasonable water allocation schemes can thus be formulated based on the resulting information of detailed effects from various impact factors and their interactions. Consequently, maximized net system benefit can be achieved.

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Section: Ftspmentioning

confidence: 99%

Section: Ftspmentioning

confidence: 99%

Factorial two-stage stochastic programming for water resources management

Zhou

Huang

2010

Stoch Environ Res Risk Assess

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“…Over the past decades, a wide range of mathematical techniques have been developed to examine the temporal and spatial economic, environmental and ecological impacts of alternative pollution-control actions, and thus aid the planners or decision-makers in formulating and adopting cost-effective water-quality management plans and policies (Bouwer 2003;Bobba et al 2004;Chen 2007;Li et al 2007;Qin et al 2008a). Among numerous management models, the multi-point-source waste reduction (MWR) model is commonly used for limiting the amount of waste discharged into water bodies (Loucks and Lynn 1966;Loucks et al 1981Loucks et al , 1985Tung and Hathhorn 1989).…”

Section: Introductionmentioning

confidence: 98%

An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management

Qin

Huang

2008

Water Resour Manage

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Water quality management is complicated with a variety of uncertainties and nonlinearities. This leads to difficulties in formulating and solving the resulting inexact nonlinear optimization problems. In this study, an inexact chanceconstrained quadratic programming (ICCQP) model was developed for stream water quality management. A multi-segment stream water quality (MSWQ) simulation model was provided for establishing the relationship between environmental responses and pollution-control actions. The relationship was described by transformation matrices and vectors that could be used directly in a multi-point-source waste reduction (MWR) optimization model as water-quality constraints. The interval quadratic polynomials were employed to reflect the nonlinearities and uncertainties associated with wastewater treatment costs. Uncertainties associated with the waterquality parameters were projected into the transformation matrices and vectors through Monte Carlo simulation. Uncertainties derived from water quality standards were characterized as random variables with normal probability distributions. The proposed ICCQP model was applied to a water quality management problem in the Changsha section of the Xiangjiang River in China. The results demonstrated that the proposed optimization model could effectively communicate uncertainties into the optimization process, and generate inexact solutions containing a spectrum of wastewater treatment options. Decision alternatives could then be obtained by adjusting different combinations of the decision variables within their solution intervals.Solutions from the ICCQP model could be used to analyze tradeoffs between the wastewater treatment cost and system-failure risk due to inherent uncertainties. The results are valuable for supporting decision makers in seeking cost-effective water management strategies.

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“…This has led to a wide range of uncertainties (Beven, 2006;Hunter et al, 2007;He et al, 2012;Ahmadi et al, 2015;Li et al, 2015;Chen et al, 2018;Shen et al, 2018). It is desirable that such parameters be treated as random input field(s) and then the related governing equations would be correspondingly turned into stochastic (Aronica et al, 2002;Qin et al, 2008;Huang and Qin, 2014). Over the past years, many stochastic approaches were developped to deal with uncertainties originated from spatial variability in flood inundation modelling (Aronica et al, 2002;Van Vuren et al, 2005;Fan and Huang, 2012;Yu et al, 2013;Wang et al, 2018;Wu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Pseudospectral Collocation Approach for Flood Inundation Modelling with Random Input Fields

Huang¹,

Qin²

2016

J ENV INFORM

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ABSTRACT. In this study, an efficient framework of pseudospectral collocation approach combined with the generalized polynomial chaos (gPC) and Karhunen-Loevè expansion (gPC/KLE) was introduced to examine the flood flow fields within a two-dimensional flood modelling system. In the proposed framework, the heterogeneous random input field (logarithmic Manning's roughness) was approximated by the normalized KLE and the output field of flood flow depth was represented by the gPC expansion, whose coefficients were obtained with a nodal set construction via Smolyak sparse grid quadrature. In total, 3 scenarios (with different levels of input spatial variability) were designed for gPC/KLE application and the results from Monte Carlo simulations were provided as the benchmark for comparison. This study demonstrated that the gPC/KLE approach could predict the statistics of flood flow depth (i.e., means and standard deviations) with significantly less computational requirement than MC; it also outperformed the probabilistic collocation method (PCM) with KLE (PCM/KLE) in terms of fitting accuracy. This study made the first attempt to apply gPC/KLE to flood inundation field and evaluated the effects of key parameters (like the number of eigenpairs and the order of gPC expansion) on model performances.

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Modeling Groundwater Contamination under Uncertainty: A Factorial-Design-Based Stochastic Approach

Cited by 25 publications

References 12 publications

Factorial two-stage stochastic programming for water resources management

Factorial two-stage stochastic programming for water resources management

An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management

A Pseudospectral Collocation Approach for Flood Inundation Modelling with Random Input Fields

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