Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

Djurović, Nevenka; Domazet, Milka; Stričević, Ružica; Pocuca, Vesna; Spalević, Velibor; Pivić, Radmila; Gregorić, Enika; Domazet, Uros

doi:10.1155/2015/742138

Cited by 38 publications

(15 citation statements)

References 46 publications

(47 reference statements)

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“…The artificial neural network (ANN) model is a data-driven model [41,46]. The ANN is one of the artificial intelligence techniques that simulates the actions of the human brain with the help of neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Three of the built-in functions available in MATLAB (Levenberg Marquardt (LM), Bayesian regularization, and scaled conjugate gradient) were tested. The LM algorithm combines the merits of two training algorithms, namely the steepest descent and Gaussian-Newton methods, and searches for the global minima function to optimize the solution [41,[47][48][49]. Bayesian regularization is a highly robust training algorithm for simulating the short-term fluctuations in groundwater levels.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Hence, three ANN and an ANFIS models were investigated in this study and the best one was used for long-term predictions of groundwater-levels. It is worth mentioning here that most of the modelling based on ANN/ANFIS in past research dealt with only the short-term predictions of groundwater-levels [34][35][36][37]41,42]. Khedri et al [42].…”

mentioning

confidence: 99%

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Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

Almuhaylan

Ghumman

Al-Salamah

et al. 2020

Water

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In arid regions, the groundwater drawdown consistently increases, and even for a constant pumping rate, long-term predictions remain a challenge. The present research applies the modular three-dimensional finite-difference groundwater flow (MODFLOW) model to a unique aquifer facing challenges of undefined boundary conditions. Artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS) have also been investigated for predicting groundwater levels in the aquifer. A framework is developed for evaluating the impact of various scenarios of groundwater pumping on aquifer depletion. A new code in MATLAB was written for predictions of aquifer depletion using ANN/ANFIS. The geotechnical, meteorological, and hydrological data, including discharge and groundwater levels from 1980 to 2018 for wells in Qassim, were collected from the ministry concerned. The Nash–Sutcliffe efficiency and mean square error examined the performance of the models. The study found that the existing pumping rates can result in an alarming drawdown of 105 m in the next 50 years. Appropriate water conservation strategies for maintaining the existing pumping rate can reduce the impact on aquifer depletion by 33%.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

Almuhaylan

Ghumman

Al-Salamah

et al. 2020

Water

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“…The Root Mean Square Error (RMSE) of the network output is the objective function to be minimized by the algorithm. The Levenberg-Marquardt algorithm was presented and substituted for the backpropagation algorithm in training neural networks and involved in studies such as predicting time series [64,65]. The Levenberg-Marquardt algorithm approximates the Newton's method without computing the Hessian matrix provided that the performance function has the form of a sum of squares in an ANN.…”

Section: Training Of the Ann With Abc (Ann-abc) And Lm (Ann-lm)mentioning

confidence: 99%

Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

Gökkuş

Yıldırım

Aydın

2017

Discrete Dynamics in Nature and Society

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Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

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“…ReLU was adopted here (see Fig. 1) as it is known to provide faster convergence with gradient descent algorithms than conventional saturating activation functions such as sigmoid and tanh that have been used in previous groundwater applications of ANNs (Djurovic et al, 2015;Tao et al, 2015;Uddameri, 2007;Nayak et al, 2006).…”

Section: Ist-ann Model Specification 135mentioning

confidence: 99%

Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling

Ghaseminejad¹,

Uddameri²

2020

Preprint

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An integrated space-time Artificial Neural Network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Modelindependent entropy measures and random forest (RF) based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, 5-fold cross-validation, and adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30,691 km 2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 5 38 independent wells during the 1956-2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009)(2010)(2011)(2012)(2013)(2014)(2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provided groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process.

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Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

Cited by 38 publications

References 46 publications

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

Physics-inspired integrated space-time Artificial Neural Networks for regional groundwater flow modeling

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