A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction

Abstract The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inp...

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Autores principales:	Peifeng Li, Pei Hua, Dongwei Gui, Jie Niu, Peng Pei, Jin Zhang, Peter Krebs
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c140c50e41a94f9eb263590406608f21
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spelling	oai:doaj.org-article:c140c50e41a94f9eb263590406608f212021-12-02T18:50:59ZA comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction10.1038/s41598-020-70438-82045-2322https://doaj.org/article/c140c50e41a94f9eb263590406608f212020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70438-8https://doaj.org/toc/2045-2322Abstract The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.Peifeng LiPei HuaDongwei GuiJie NiuPeng PeiJin ZhangPeter KrebsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peifeng Li Pei Hua Dongwei Gui Jie Niu Peng Pei Jin Zhang Peter Krebs A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
description	Abstract The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.
format	article
author	Peifeng Li Pei Hua Dongwei Gui Jie Niu Peng Pei Jin Zhang Peter Krebs
author_facet	Peifeng Li Pei Hua Dongwei Gui Jie Niu Peng Pei Jin Zhang Peter Krebs
author_sort	Peifeng Li
title	A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
title_short	A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
title_full	A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
title_fullStr	A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
title_full_unstemmed	A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
title_sort	comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c140c50e41a94f9eb263590406608f21
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A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction

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