A Neural‐Network Approach to the Determination of Aquifer Parameters

Aziz, A. Rashid A.; Wong, Kaufui V.

doi:10.1111/j.1745-6584.1992.tb01787.x

Cited by 82 publications

(37 citation statements)

References 7 publications

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“…For example, Coulibaly et al (2000) applied ANNs to forecast reservoir inflow and river flow prediction. The ANNs methodology has also been applied to determine aquifer parameters (Abd Aziz and Wong, 1992). Maier and Dandy (1996) used ANNs to predict water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

Lallahem

Mania

2003

Hydrological Processes

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Abstract:The purpose of this research is to include expert knowledge as one part of the modelling system and therefore offer the chance to create a productive interaction system between expert, mathematical model (MMO8) and artificial neural networks (ANNs). In the present project, the first objective is to determine some parameters by the MMO8 model, introduced as ANN input parameters to forecast spring outflow. The second objective is first to investigate the effect of temporal information by taking current and past data sets and then to forecast spring outflow. The good results obtained reveal the merit of the ANNs-MMO8 combination, and specifically multilayer perceptron (MLP) models. This methodology, for a network with lower, lag and number hidden layer, consistently produced better performance.

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Section: Introductionmentioning

confidence: 99%

Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

Lallahem

Mania

2003

Hydrological Processes

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“…The study area comprises a total area of about 770 km 2 . From the hydrological cross section, which pass through Wadi El Natrun area in West-East direction.…”

Section: Study Area (Wadi El Natrun)mentioning

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%

“…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%

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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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“…have been adopted by researchers to reduce the computation burden. Neural network has been used widely in the area of water resources (Aziz and Wong [12]) and thus used in this study as a proxy simulator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of in-Situ Permeable Reactive Barrier

Mathura¹,

Pandeyb²

2017

WIT Transactions on Ecology and the Environment

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The study provides a methodology of obtaining optimal design parameters of Permeable Reactive Barriers (PRB) through a multi-objective optimization problem involving the cost and time of remediation. A PRB is an in-situ eco-friendly remediation technology that comprises reactive material that helps remove heavy metals and petroleum hydrocarbons from groundwater. A numerical model is developed in the study to solve the contaminant transport equation that is validated and thereafter used to simulate cases of contamination due to heavy metals. Subsequently, the required length and width of the continuous reactive barrier are determined from the plot of maximum relative concentrations. The impact of various hydraulic parameters on the design parameters as well as the cost of PRB is then studied following which the maximum length and width of the barrier as well as the total cost to clean up is determined. A similar exercise is further conducted for BTEX contaminated site after validating the developed model. A PRB is placed at varying distances from the source of pollution and the corresponding length, width and time for remediation computed. Following this, a multi-objective algorithm is developed to minimize the two objectives namely: the cost and time for remediation. Two different Hybrid algorithms are developed combining various algorithms and a set of Paretos of cost and time of remediation for different population sizes is computed using Artificial Neural Networks (ANN). The performance of the algorithms is then analysed using two performance analysers. A set of optimal solutions for the length, width, distance of barrier from the source and the corresponding cost and time of remediation are subsequently computed.

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A Neural‐Network Approach to the Determination of Aquifer Parameters

Cited by 82 publications

References 7 publications

Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

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

Optimal Design of in-Situ Permeable Reactive Barrier

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