Institute of Urban and Industrial Water Management, Technische Universität Dresden, 01062, Dresden, Germany.
Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou, 510006, China.
Sci Rep. 2020 Aug 10;10(1):13439. doi: 10.1038/s41598-020-70438-8.
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.
有毒金属在水生环境中的出现是由多种污染造成的，这使得浓度预测变得困难。在本研究中，应用了传统的反向传播神经网络（BPNN）和具有外部输入的非线性自回归网络（NARX）作为基准模型。Fe、pH、电导率、水温和河流流量、硝酸盐氮和溶解氧等解释变量被用作不同的输入组合，以预测 As、Pb 和 Zn 的长期浓度。小波变换被应用于分解时间序列数据，然后与传统方法（如 WNN 和 WNARX）相结合。将 WNN 和 WNARX 的混合模型的建模性能与传统模型进行了比较。所有给定的模型都通过一个 18 年的数据集进行了训练、验证和测试，并基于一个 2 年的数据集的模拟结果进行了演示。结果表明，所给模型对 As、Pb 和 Zn 的有毒金属的长期预测表现出一般较好的性能。小波变换可以提高长期浓度预测的效果。然而，它对于每种金属的预测并不一定有用。因此，对于不同的金属预测，应该使用不同的输入模型来获得最佳的预测效果。
