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 co

SUBMITTER: Li P 

PROVIDER: S-EPMC7417571 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature