Parameter Estimation in Groundwater Hydrology Using Artificial Neural Networks

Shigidi, Abdalla M. T.; Garcia, Luis A.

doi:10.1061/(asce)0887-3801(2003)17:4(281)

Cited by 40 publications

(26 citation statements)

References 18 publications

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“…The determination of aquifer parameters (also termed as inverse problem) has always been a challenge because of its ill-posedness [2][3][4][5][6][7]. Inversion of the trained feed forward neural network is done to estimate the transmissivity field for synthetic problem [5]. Most of the papers used synthetic or published data to assess parameters in confined aquifer [4][5][6]8] for the reason of nonavailability of sufficient number of patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The determination of aquifer parameters (also termed as inverse problem) has always been a challenge because of its ill-posedness [2][3][4][5][6][7]12,13]. ANNs is solving many complex real world-predicting problems.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the papers used synthetic or published data to assess parameters in confined aquifer [4][5][6]8] for the reason of nonavailability of sufficient number of patterns. Model sensitivity in terms of number of nodes in hidden layers is carried out by Shigdi et al [5] using correlation coefficient, Y-intercept, and slope. An approach with a combination of ANNs and type curves based on Papadopoulos and Cooper analytical solution was used by Balkhair [4] whereas ANN and This solution coupling was performed by Lin GF et al [9].…”

Section: Introductionmentioning

confidence: 99%

“…Inversion of the trained feed forward neural network is done to estimate the transmissivity field for synthetic problem [5]. Most of the papers used synthetic or published data to assess parameters in confined aquifer [4][5][6]8] for the reason of nonavailability of sufficient number of patterns. Model sensitivity in terms of number of nodes in hidden layers is carried out by Shigdi et al [5] using correlation coefficient, Y-intercept, and slope.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the efforts are on increase to assess these by non-field methods using the technique of ANNs. The determination of aquifer parameters (also termed as inverse problem) has always been a challenge because of its ill-posedness [2][3][4][5][6][7]. Inversion of the trained feed forward neural network is done to estimate the transmissivity field for synthetic problem [5].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Identification of Hydraulic Parameters of Wadi El Natrun Pliocene Aquifer Using Artificial Neural Network

Khalaf¹,

Ao²,

Mg³

et al. 2017

Hydrol Current Res

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Many techniques, approaches and tools were used in this Research to achieve the Methodology. Using artificial neural network to simultaneous hydraulic parameters is one of these techniques. Transmissivity and storativity consider the most important parameter in each aquifer due to the reality of their effect on the aquifer properties. In this research, it is assumed that the transmissivity (T) and the storativity (S), represented by coordinates (X), (Y), hydraulic head (H), and observation times (t). These variables were chosen depending on the literature review. In the present study, the hydraulic head values at each cell (H) and the location of the cells (x, y) are considered as input parameters for finding the unknown parameters. The transmissivity (T) and storativity values (S) at cells are assumed and used in the finite difference method (Forward model) in order to find the value of hydraulic head at that cell. The hydraulic head values were used in the artificial neural networks (Inverse model) to estimate transmissivity (T) and storativity values (S) for Wadi El Natrun Depression. The study is based on coupling of forward model and inverse model. In general, the parameter estimation process consists of identifying a model that would reverse a complex forward relation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification of Hydraulic Parameters of Wadi El Natrun Pliocene Aquifer Using Artificial Neural Network

Khalaf¹,

Ao²,

Mg³

et al. 2017

Hydrol Current Res

View full text Add to dashboard Cite

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Inverse Methods for Parameter Estimations

Sun

2005

Encyclopedia of Hydrological Sciences

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Estimation of various flow and mass transport parameters can be seen as the inverse problem of groundwater modeling. In this article, current methodologies for parameter estimation are classified according to whether they consider the errors in observation data, in parameter structure, and in model applications. The structures of hydraulic parameters are usually very complex and unknown. This article gives an overview on adaptive parameterization methods for simultaneously identifying the parameter values, the complexity level, and the pattern of parameter structure. Methods for model reliability analysis are described. Finally, a generalized inverse problem is introduced that can find a representative parameter for a given model application. The Inverse ProblemFor a model given by (2), the observed values of state variables, u obs , at observation locations and times x obs can Encyclopedia of Hydrological Sciences. Edited by M G Anderson.

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Application of the Artificial Neural Network (ANN) in Predicting Anode Properties

Bhattacharyay

Kocaefe

et al. 2013

Light Metals 2013

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Carbon anodes are a major part of the cost of primary aluminum production. The focus of the industry is to minimize the consumption of anodes by improving their quality. Therefore, the determination of the impact of quality of raw materials as well as process parameters on baked anode properties is important. The plants have a large data base which, upon appropriate analysis, could help maintain or improve the anode quality. However, it is complex and difficult to analyze these data using conventional methods. The artificial neural network (ANN) is a mathematical tool that can handle such complex data. In this work, Matlab software was used to develop a number of ANN models. Using published data, linear multi-variable analysis and ANN were applied to assess the advantages of custom multilayered feed-forward ANN. Results are presented which show a number of industrial applications.

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Parameter Estimation in Groundwater Hydrology Using Artificial Neural Networks

Cited by 40 publications

References 18 publications

Identification of Hydraulic Parameters of Wadi El Natrun Pliocene Aquifer Using Artificial Neural Network

Identification of Hydraulic Parameters of Wadi El Natrun Pliocene Aquifer Using Artificial Neural Network

Inverse Methods for Parameter Estimations

Application of the Artificial Neural Network (ANN) in Predicting Anode Properties

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