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 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Li P,Hua P,Gui D,Niu J,Pei P,Zhang J,Krebs P

doi

10.1038/s41598-020-70438-8

subject

Has Abstract

pub_date

2020-08-10 00:00:00

pages

13439

issue

1

issn

2045-2322

pii

10.1038/s41598-020-70438-8

journal_volume

10

pub_type

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