Groundwater level forecasting using artificial neural networks

Daliakopoulos, Ioannis Ν.; Coulibaly, Paulin; Tsanis, Ioannis K.

doi:10.1016/j.jhydrol.2004.12.001

Cited by 568 publications

(264 citation statements)

References 15 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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“…Moreover, one of the main benefits of BR is that it precludes over-training in the network. The previous studies successfully applied this algorithm to train ANN to solve different problems (32,33). The mathematical formulation of the MLP can be expressed as follows:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

Alizamir

Sobhanardakani

2018

Jundishapur J Health Sci

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Background: The quality of groundwater as the most important source for domestic, irrigation, and industrial purposes is affected by discharge of the chemicals from the anthropogenic resources. Therefore, the current study aimed at predicting heavy metals (As, Pb, Cu, and Zn) contamination in groundwater resources of Toyserkan Plain as an important agricultural area in Hamedan Province, West of Iran using artificial neural network -particle swarm optimization (ANN-PSO) approach. Methods: In the current study, samples were randomly selected from 20 groundwater wells with depth of 10 -90 m. The samples were filtered and kept cool in polyethylene bottles and then taken for the analysis of metal contents; they were acidified using nitric acid to reach pH < 2. Finally, element contents were determined using inductively coupled plasma -optical emission spectrometry (ICP-OES). Also, the performance of the PSO model was compared with that of ANN using Bayesian regulation (BR) training algorithm in terms of accuracy and model prediction efficiency.

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“…The gradient-descent method, along with the chain rule of differentiation, is generally employed to modify the network weights as : where Δv ij (n) and Δv ij (n−1) = the weight increments between node i and j during the n th and (n-1) th pass or epoch; δ = the learning rate; and α = the momentum factor. This study adopted the hyperbolic tangent activation function ) and the training algorithm of Levenberg-Marquardt (LM) (Daliakopoulos et al 2005;Nourani et al 2008aNourani et al , 2008bMustafa et al 2012). …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Tayfur

Nadiri

Moghaddam

2014

Water Resour Manage

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Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.

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Groundwater level forecasting using artificial neural networks

Cited by 568 publications

References 15 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

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

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