Estimation of parameters in groundwater modelling by modified Clonalg

Eryiğit, Miraç

doi:10.2166/hydro.2021.139

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The proposed model was applied in two hypothetical cases. Results presented the feasibility of the model in the estimation of groundwater parameters (Eryigȋt 2021).…”

Section: Introductionmentioning

confidence: 92%

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Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

Mohtashami

Monfared

Azizyan

et al. 2021

Journal of Hydroinformatics

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In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer.

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“…The proposed model was applied in two hypothetical cases. Results presented the feasibility of the model in the estimation of groundwater parameters (Eryigȋt 2021).…”

Section: Introductionmentioning

confidence: 92%

“…Maximum likelihood function is calculated in the MLPG-MK model. This function is presented in the following equation (Field et al 2016):…”

Section: Groundwater Flow Equation In the Unconfined Aquifermentioning

confidence: 99%

Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

Mohtashami

Monfared

Azizyan

et al. 2021

Journal of Hydroinformatics

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Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques

Shukla

Kumar

Vishwakarma

et al. 2021

Theor Appl Climatol

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The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge.The daily data of 12 years (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

Elbeltagi

Pande

Kumar³

et al. 2023

Environ Sci Pollut Res

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Estimation of parameters in groundwater modelling by modified Clonalg

Cited by 6 publications

References 19 publications

Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

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