Predicting the concentration of sulfate (SO42–) in drinking water using artificial neural networks: a case study: Médéa-Algeria

Tahraoui, Hichem; Belhadj, Abd-Elmouneïm; Hamitouche, A.; Bouhedda, Mounir; Amrane, Abdeltif

doi:10.5004/dwt.2021.26813

Cited by 25 publications

(62 citation statements)

References 39 publications

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“…An artificial neural network (ANN) is an advanced, nonlinear, and empirical model comprising a multitude of units called neurons [ 14 , 15 ]. These neurons work together to form the network, and the functionality of the network is primarily determined by the connections between the neurons [ 16 , 17 ]. The neurons in an ANN are divided into three separate layers: the input layer, the output layer, and the hidden layer [ 18 , 19 ].…”

Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R2 = 0.99998 and MSE = 5.95 × 10−3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

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Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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“…Yet they are heavily used in the chemical industry. The sulfate concentration in freshwater should be between 3 and 30 mg per liter [27].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Quality of Water According to a Random Forest Classifier

Alomani¹,

Alhawiti²,

Alhakamy³

2022

IJACSA

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Potable or drinking water is a daily life necessity for humans. The safety of this water is a concern in many regions around the world, since polluted waters are increasing and causing the spread of disease among populations. Continuous management and evaluation of the water which is meant for drinking is very essential and must be taken seriously. Often, the quality of water is evaluated through regular laboratory testing and analysis which can be tiresome and time consuming. On the other hand, advanced technologies using big data with the help of machine learning can have better results in terms of potability evaluation. For this reason, several studies have been conducted on predicting the quality of water and the several factors and classification that affect the prediction model. In this study, a random forest model was developed using PySpark classification to predict the potability of river water by relying on ten different features: pH, hardness, presence of solids, presence of chloramines, presence of sulfate, conductivity, organic carbon, trihalomethanes, turbidity, and finally potability. In addition, The developed model was able to predict water potability classification with a 1.0 accuracy, and 1.0 F1-score.

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“…Li et al, 2021;Gunathilake et al, 2021;Huang et al, 2021;Kilinc, 2022;Kilinc & Haznedar, 2022;Kim et al, 2022;Niu & Feng, 2021;Rahimzad et al, 2021), water quality predictions (Abba et al, 2017;W. Li et al, 2020;Sami et al, 2021;Stamenković, 2021;Tahraoui et al, 2021;Y.-F. Zhang et al, 2020), and drought prediction (Adamowski et al, 2012;Ahmadi et al, 2021;Dikshit et al, 2020;M.M.H. Khan et al, 2020;Nabipour et al, 2020;Nourani et al, 2019).…”

Section: Artificial Neural Networkunclassified

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

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The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

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Predicting the concentration of sulfate (SO42–) in drinking water using artificial neural networks: a case study: Médéa-Algeria

Cited by 25 publications

References 39 publications

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Prediction of Quality of Water According to a Random Forest Classifier

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

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