Water quality modelling using artificial neural network and multivariate statistical techniques

Isiyaka, Hamza Ahmad; Mustapha, Adamu; Juahir, Hafizan; Phil-Eze, Philip Ogbonnia

doi:10.1007/s40808-018-0551-9

Cited by 85 publications

(36 citation statements)

References 41 publications

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“…ANN mimics the behaviour of the human brain in processing information and can learn, predict and correlate the pattern of experimental data when subjected to training [ 35 , 36 ]. The technique provides a platform that can determine the impact of some optimized adsorption parameters in the behaviour of a target output.…”

Section: Methodsmentioning

confidence: 99%

Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models

et al. 2021

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Effective removal of 4-chloro-2-methylphenoxyacetic acid (MCPA), an emerging agrochemical contaminant in water with carcinogenic and mutagenic health effects has been reported using hydrothermally synthesized MIL-101(Cr) metal-organic framework (MOF). The properties of the MOF were ascertained using powdered X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermal gravimetric analysis (TGA), field emission scanning electron microscopy (FESEM) and surface area and porosimetry (SAP). The BET surface area and pore volume of the MOF were 1439 m 2 g −1 and 0.77 cm 3 g −1 , respectively. Artificial neural network (ANN) model was significantly employed for the accurate prediction of the experimental adsorption capacity ( q e ) values with minimal error. A rapid removal of the pollutant (99%) was recorded within short time (approx. 25 min), and the reusability of the MOF (20 mg) was achieved up to six cycles with over 90% removal efficiency. The kinetics, isotherm and thermodynamics of the process were described by the pseudo-second-order, Freundlich and endothermic adsorption, respectively. The adsorption process is spontaneous based on the negative Gibbs free energy values. The significant correlation between the experimental findings and simulation results suggests the great potential of MIL-101(Cr) for the remediation of MCPA from water matrices.

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Section: Methodsmentioning

confidence: 99%

Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models

et al. 2021

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“…The partial least squares regression (PLSR) (Wold, 1966), as a typical linear regression method, is of wide use in model construction for its advantage of using all available bands without multi-collinearity problem. With the gradual promotion of machine learning algorithms, such advanced semi-empirical machine learning algorithms (Keller et al, 2018) as artificial neural networks (Isiyaka et al, 2018; Bansal & Ganesan, 2019), support vector machine, extreme learning machine (ELM) have also been gradually applied in the retrieval of water quality parameters, which have greatly improved the model inversion accuracy. Compared with traditional neural networks, ELM calculates faster with the learning accuracy guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

Zheng

Chen

Zhao

et al. 2019

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Water pollution has been hindering the world’s sustainable development. The accurate inversion of water quality parameters in sewage with visible-near infrared spectroscopy can improve the effectiveness and rational utilization and management of water resources. However, the accuracy of spectral models of water quality parameters is usually prone to noise information and high dimensionality of spectral data. This study aimed to enhance the model accuracy through optimizing the spectral models based on the sensitive spectral intervals of different water quality parameters. To this end, six kinds of sewage water taken from a biological sewage treatment plant went through laboratory physical and chemical tests. In total, 87 samples of sewage water were obtained by adding different amount of pure water to them. The raw reflectance (Rraw) of the samples were collected with analytical spectral devices. The Rraw-SNV were obtained from the Rraw processed with the standard normal variable. Then, the sensitive spectral intervals of each of the six water quality parameters, namely, chemical oxygen demand (COD), biological oxygen demand (BOD), NH3-N, the total dissolved substances (TDS), total hardness (TH) and total alkalinity (TA), were selected using three different methods: gray correlation (GC), variable importance in projection (VIP) and set pair analysis (SPA). Finally, the performance of both extreme learning machine (ELM) and partial least squares regression (PLSR) was investigated based on the sensitive spectral intervals. The results demonstrated that the model accuracy based on the sensitive spectral ranges screened through different methods appeared different. The GC method had better performance in reducing the redundancy and the VIP method was better in information preservation. The SPA method could make the optimal trade-offs between information preservation and redundancy reduction and it could retain maximal spectral band intervals with good response to the inversion parameters. The accuracy of the models based on varied sensitive spectral ranges selected by the three analysis methods was different: the GC was the highest, the SPA came next and the VIP was the lowest. On the whole, PLSR and ELM both achieved satisfying model accuracy, but the prediction accuracy of the latter was higher than the former. Great differences existed among the optimal inversion accuracy of different water quality parameters: COD, BOD and TN were very high; TA relatively high; and TDS and TH relatively low. These findings can provide a new way to optimize the spectral model of wastewater biochemical parameters and thus improve its prediction precision.

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“…ANN is an intelligent modelling technique that mimics the behaviour of the biological nervous system (brain) in processing information. 43,44 ANN can learn and predict the pattern of experimental data when subjected to training, which allows it to model the complex non-linear relationship between the independent and dependent variables. 45 ANN has the ability to predict, cluster, optimize and apportion the impact of certain optimization parameters in the behaviour of an expected output.…”

Section: Articial Neural Network (Ann)mentioning

confidence: 99%

Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies

et al. 2020

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Water quality modelling using artificial neural network and multivariate statistical techniques

Cited by 85 publications

References 41 publications

Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models

Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models

Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies

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