Temporal modelling of long-term heavy metal concentrations in aquatic ecosystems

Abstract

This paper examines a series of connected and isolated lakes in the UK as a model system with historic episodes of heavy metal contamination. A 9-year hydrometeorological dataset for the sites was identified to analyse the legacy of heavy metal concentrations within the selected lakes based on physico-chemical and hydrometeorological parameters, and a comparison of the complementary methods of multiple regression, time series analysis, and artificial neural network (ANN). The results highlight the importance of the quality of historic datasets without which analyses such as those presented in this research paper cannot be undertaken. The results also indicate that the ANNs developed were more realistic than the other methodologies (regression and time series analysis) considered. The ANNs provided a higher correlation coefficient and a lower mean squared error when compared to the regression models. However, quality assurance and pre-processing of the data were challenging and were addressed by transforming the relevant dataset and interpolating the missing values. The selection and application of the most appropriate temporal modelling technique, which relies on the quality of available dataset, is crucial for the management of legacy contaminated sites to guide successful mitigation measures to avoid significant environmental and human health implications.

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

“…Feed-forward ANNs are able to model nonlinear complex environmental systems [55]. Additionally, as stated by Bushra et al [56], back-propagation ANNs have the merit of being simple to adapt, and no tuning or learning is required for their parameter and function features. Furthermore, as it is stated by Brown et al [57], ANN models give more reliable outputs in comparison to other machine learning methods (e.g., decision trees or linear regression) when the number of data measurements is relatively small, like in our case.…”