Sensitivity analysis of the probability distribution of groundwater level series based on information entropy

Wang, Dong; Wu, Jichun

doi:10.1007/s00477-012-0556-2

Cited by 35 publications

(18 citation statements)

References 39 publications

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“…Successful assessment of groundwater resources depends on reliable and stable groundwater simulation. Therefore, as a result of the uncertainty of groundwater modeling, decision-makers have to face the risk of failure in making decisions [4]. Uncertainty analysis includes studying the origin, transport and producing processes of uncertainties, describing and evaluating the state and characteristics of uncertainties.…”

Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

Review of the uncertainty analysis of groundwater numerical simulation

Zeng

2013

Chin. Sci. Bull.

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Groundwater system is a complex and open system, which is affected by natural conditions and human activities. Natural hydrological processes is conceptualized through relatively simple flow governing equations in groundwater models. Moreover, observation data is always limited in field hydrogeological conditions. Therefore, the predictive results of groundwater simulation often deviate from true values, which is attribute to the uncertainty of groundwater numerical simulation. According to the process of system simulation, the uncertainty sources of groundwater numerical simulation can be divided into model parameters, conceptual model and observation data uncertainties. In addition, the uncertainty stemmed from boundary conditions is sometimes refered as scenario uncertainty. In this paper, the origination and category of groundwater modeling uncertainty are analyzed. The recent progresses on the methods of groundwater modeling uncertainty analysis are reivewed. Furthermore, the researches on the comprehensive analysis of uncertainty sources, and the predictive uncertainty of model outputs are discussed. Finally, several prospects on the deveolpment of groundwater modeling uncetainty analysis are proposed.groundwater modeling, uncertainty analysis, model parameter, conceptual model, observation data Citation:

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Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

Review of the uncertainty analysis of groundwater numerical simulation

Zeng

2013

Chin. Sci. Bull.

Self Cite

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“…In mutual entropy method, the sensitivity of the output to input variables is estimated by the following two indicators [24].…”

Section: X Y H X H X Y H Y H Y X H X H Y H X Ymentioning

confidence: 99%

“…Mishra et al [23] described three global sensitivity methods, that is, stepwise regression, mutual information analysis and classification tree for determining uncertainty importance, and showed some sample applications for ground water models. Zeng et al [24] compared stepwise regression analysis and mutual entropy analysis for identifying the key uncertainty variables affecting the parameters of normal groundwater level series.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information

Wang

Xiong

et al. 2014

Entropy

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Abstract:The intention of this paper is to evaluate the sensitivity of the Storm Water Management Model (SWMM) output to its input parameters. A global parameter sensitivity analysis is conducted in order to determine which parameters mostly affect the model simulation results. Two different methods of sensitivity analysis are applied in this study. The first one is the partial rank correlation coefficient (PRCC) which measures nonlinear but monotonic relationships between model inputs and outputs. The second one is based on the mutual information which provides a general measure of the strength of the non-monotonic association between two variables. Both methods are based on the Latin Hypercube Sampling (LHS) of the parameter space, and thus the same datasets can be used to obtain both measures of sensitivity. The utility of the PRCC and the mutual information analysis methods are illustrated by analyzing a complex SWMM model. The sensitivity analysis revealed that only a few key input variables are contributing significantly to the model outputs; PRCCs and mutual information are calculated and used to determine and rank the importance of these key parameters. This study shows that the partial rank correlation coefficient and mutual information analysis can be considered effective methods for assessing the sensitivity of the SWMM model to the uncertainty in its input parameters.

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“…He et al (2011) analyzed the parameter sensitivity of the SNOW17 model using the Spearman's rank correlation coefficient method, and the rankings of parameters were determined using the results of significance testing. Zeng et al (2012) used stepwise regression analysis and mutual entropy analysis method to assess the uncertainty parameters of probability density function of groundwater level series. Regression analysis also has been used in other hydrological models, such as SWAT (Muleta and Nicklow, 2005), SWMM (Wang et al, 2008), HYMOD (Yang, 2011), SAC-SMA (Gan et al, 2014).…”

Section: Regression Methodsmentioning

confidence: 99%

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

470

273

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s u m m a r y Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.

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Sensitivity analysis of the probability distribution of groundwater level series based on information entropy

Cited by 35 publications

References 39 publications

Review of the uncertainty analysis of groundwater numerical simulation

Review of the uncertainty analysis of groundwater numerical simulation

Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

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