Using neural networks for parameter estimation in ground water

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

doi:10.1016/j.jhydrol.2005.05.028

Cited by 90 publications

(49 citation statements)

References 23 publications

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“…They are capable of providing a neuron computing approach to solve complex problems. In the last decade, ANNs have been widely successfully applied to various water resources problems, such as hydrological processes (Nayak et al 2004;Sahoo et al 2005;Dastorani et al 2010;Guo et al 2011;Wu and Chau 2011;Senkal et al 2012), water resources management (Kralisch et al 2003;Sreekanth and Datta 2010), groundwater problems (Daliakopoulos et al 2005;Dixon 2005;Garcia and Shigidi 2006;Nayak et al 2006;Ghose et al 2010;Banerjee et al 2011), and water quality (Ha and Stenstrom 2003;Kuo et al 2006;Anctil et al 2009;da Costa et al 2009;Dogan et al 2009;Chang et al 2010;He et al 2011). ANNs also have been used for modeling and forecasting DO (Kuo et al 2007;Singh et al 2009;Ranković et al 2010;Najah et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl . Cl − was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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“…The first part of this problem was run to get a steady state solution that takes the form: this process is cycled until the convergence is met. On the other hand, Garcia et al [17] solved the same problem in their previous study-i.e., Shigidi et al, by adding a noise term to the observed hydraulic heads and they obtained more precise results than their previous study [5].…”

Section: Govering Equationmentioning

confidence: 85%

“…ANNs is solving many complex real world-predicting problems. ANNs have been applied to predicting groundwater levels [14,15], precipitation and runoff modeling, and aquifer parameter estimations [3,5,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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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“…Each of the hidden layer nodes (neurons) computes a weighted sum of the inputs, passes the sum through the transfer (activation) function, and presents the results to the next layer until the output layer is reached. Determining the architecture of a neural network involves determining the number of layers in the network as well as the number of nodes (neurons) in each layer (Garcia and Shigidi, 2006). In this study, the training process was performed by the commercial package MATLAB, which includes a number of training algorithms including the back propagation training algorithm.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province

Sarmadian¹,

Taghizadeh–Mehrjardi²

2014

EJSS

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Article Info Received : 01.08.2013 Accepted : 29.10.2013 The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R 2 , RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data.

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Using neural networks for parameter estimation in ground water

Cited by 90 publications

References 23 publications

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

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

Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province

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