Use of artificial neural networks to evaluate the effectiveness of riverbank filtration

Sahoo, G. B.; Ray, Chittaranjan; Wang, Jack Z.; Hubbs, Stephen A.; Song, Rengao; Jasperse, J.; Seymour, D.

doi:10.1016/j.watres.2005.04.020

Cited by 22 publications

(8 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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“…Sahoo et al [15] reported that a rise in river flow causes a corresponding rise in river turbidity. A similar trend is visible for HPC and coliform.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Riverbank Filtration as Drinking Water Treatment Process Using Back-Propagation Technique

Hashem

2006

JES. Journal of Engineering Sciences

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Riverbank filtration (RBF) is a natural process, using alluvial aquifers to remove contaminants and pathogens in river water for the production of drinking water as a low-cost water treatment technology. This study illustrates the development and application of feed-forward backpropagation network (BPN) as a type of artificial neural networks. The BPN prediction results produced good agreement with measured data at a correlation coefficient above 0.98 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal.

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“…The effectiveness of RBF for removing surface water contaminants depends largely on hydrogeological conditions. It is about the soil microbiology, characteristic of the bank materials and streambed, and scouring characteristic [13]. In many countries, the alluvial soil aquifers hydraulically connected to a water course would be preferred sites for drinking water production [14].…”

Section: Factors Influencing Optimization Of Rbfmentioning

confidence: 99%

Application of Artificial Barrier as Mitigation ofE. coliWhich Pass through Riverbank Filtration

Rashid¹,

Abustan²

2020

New Insight Into Brucella Infection and Foodborne Diseases

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Water security in the water treatment plant has been doubted, and the treatment process may have given unreliable and unsafe water to the public. A newspaper reported on November 19, 2011, that laboratory tests on water samples in Kelantan for each year by the Ministry of Health have found harmful bacteria including Escherichia coli (E. coli) in the water samples. More worryingly, it was stated in a study that chlorine in water treated with high chlorine can be harmful to human health. In 2010, Malaysia has begun to approach a natural treatment technique, namely, riverbank filtration (RBF), and firstly used it at the Water Treatment Plant in Jeli, Kelantan, and Kuala Kangsar, Perak. RBF limitation is the invisible groundwater flow that makes it difficult to predict the transport of contaminants. Managing groundwater is important to ensure that water is aligned in compliance with government legislation and environmental protection. Due to that, this study suggests an implementation of an artificial barrier for microorganism in RBF to sustain the good water quality abstracted from the abstraction well. This pretreatment or purifying method is to improve the effectiveness of RBF in removing pollutants during shock loads and reduce the load placed in the water treatment process.

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Use of artificial neural networks to evaluate the effectiveness of riverbank filtration

Cited by 22 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

Evaluation of Riverbank Filtration as Drinking Water Treatment Process Using Back-Propagation Technique

Application of Artificial Barrier as Mitigation ofE. coliWhich Pass through Riverbank Filtration

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