Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Abyaneh, Hamid Zare

doi:10.1186/2052-336x-12-40

Cited by 168 publications

(83 citation statements)

References 22 publications

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“…The aggregates of the ANN results obtained at each of the CTP levels-95.5%, 99.3%, 99.9% and 100%-were found by calculating the averages of the built ANN models-ANN1, ANN2, ANN3 and ANN4 (Figure 4)-at the levels. After trying between 1000 and 5000 iterations of the ANN training networks, the best networks from each CTP level was used to compute the Hit Ratio (HR), Miss Ratio (MR), Mean Square Error (MSE) and the coefficient of determination (R 2 ) of the trained and validation datasets per Equations (5)- (7) [39].…”

Section: Illustrative Example and Resultsmentioning

confidence: 99%

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems

Ossai

2017

Infrastructures

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Real-time prediction of the state of complex systems is vital for integrity management since it is easier to plan for asset maintenance, reduce risks associated with unplanned downtime and reduce the cost of maintenance. This study utilized a four-fold cross-validation ensemble for an Artificial Neural Network (ANN) that used Multi-Layer Perceptron (MLP) in a backward propagation technique for haul crane prognosis. Big data on components' degradation states obtained from the Supervisory Control And Data Acquisition (SCADA) systems were used to implement the study. After preprocessing the dataset, importance scoring was used to compute the Cumulative Target-component Percentage-influence (CTP) of the input variables (source components) on the output variable (the target component) at the 95.5%, 99.3%, 99.9% and 100% levels. The specific source components responsible for the CTP levels of the target component were later used for the ANN network training that followed the cross-validation ensemble technique. The cross-validation ensemble ANN technique was also compared to the classic ANN and other machining learning algorithms. Finally, the best-trained cross-validation ensemble ANN network, which was obtained at the 99.9% CTP level, was used for future estimation of the time of failure of the system to enhance planning for the expected maintenance program that will be required at such times.

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Section: Illustrative Example and Resultsmentioning

confidence: 99%

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems

Ossai

2017

Infrastructures

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“…The mechanism of the single factor evaluation method is that using the classification of the worst single index of water quality to determine the classification of the comprehensive water quality; the method is simple and clear, and can directly attain the relationship between water quality and evaluation criteria, but fails to get a comprehensive evaluation, furthermore, the accuracy of the evaluation results is poor. The principal component analysis (PCA) is an integrated model for water quality assessment and it can be used to establish comprehensive evaluation index and the effect is better, but it's difficult to get a better evaluation result if the participating index is too more to reduce the contribution rate of the principal component [9] [13] [14] [15] [16] [17]. Because the water quality is affected by many factors, there is a complex non-linear relationship between the evaluation index and water quality standard.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the back propagation artificial neural network (BP ANN) is used to the water environment quality evaluation model; the Radial Basis Function Artificial Neural Network (RBF ANN) is adopted to evaluate water quality. The traditional neural networks have some shortcomings, including slow convergence speed, easy to trap into local extremum, so that many improved neural network models have been successfully applied to water quality evaluation [9] [14] [21].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

Li¹,

Wu²,

Chen³

et al. 2017

CWEEE

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To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

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“…One of the paramount features of artificial neural network is their capability in handling large and complex systems with numerous interrelated parameters (Nourani et al, 2011). Artificial neural networks have successfully been utilized in a number of research concentrating on water quality prediction in lakes (Stefan et al, 1995), rivers (Singh et al, 2009;Niroobakhsh et al, 2012),reservoirs (Kuo et al, 2007;Rankovic et al, 2010;) and wastewater treatment (Abyaneh, 2014). Sarkar and Pandey (2015) modeled the water quality of River using Artificial Neural Network system.…”

Section: Introductionmentioning

confidence: 99%

PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)

Omar

2017

juod

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The purpose of this study is to develop a feed-forward neural network (FFNN) model with back-propagation learning algorithm to predict the dissolved oxygen from water temperature and 5 days-biological oxygen demand in the Tigris River, Baghdad-Iraq. The Artificial Neural Networks model was implemented utilizing measured data that were gathered from laboratories of water treatment plant, Baghdad-Iraq, during the year 2008. The correlation analysis between dissolved oxygen and dependent parameters were utilized in selecting the major inputs from water quality parameters for commencing of ANN models. The performance of ANN models were tested utilizing the coefficient of correlation (R), the efficiency coefficient of Nash-Sutcliffe (NS), mean square error (MSE) and mean absolute errors (MAE). The outputs revealed that the feed-forward neural networks using back-propagation learning algorithm which was prepared by temperature and biological oxygen demand offered a relatively high correlation coefficient of 0.885, and efficiency coefficient of 0.782, meanwhile a reasonably low mean square errors of 1.133, and mean absolute errors of 0.369 values for whole array period. The results of the present study demonstrate that the artificial neural networks using FFNN model is capable to forecast the dissolved oxygen values with acceptable accuracy. This is suggesting that the artificial neural network is a useful tool for Tigris River management in Baghdad-Iraq.

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Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Cited by 168 publications

References 22 publications

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)

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