Application of artificial neural networks to predict the heavy metal contamination in the Bartin River
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RESEARCH ARTICLE
Application of artificial neural networks to predict the heavy metal contamination in the Bartin River Handan Ucun Ozel 1 & Betul Tuba Gemici 1 & Ercan Gemici 2 & Halil Baris Ozel 3 & Mehmet Cetin 4
&
Hakan Sevik 5
Received: 28 March 2020 / Accepted: 15 July 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this study, copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), nickel (Ni), and lead (Pb) analyses were performed, and the results were modelled by artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Samples were taken from 3 stations selected on the Bartin River for 1 year between December 2012 and December 2013. Radial basis neural network (RBANN), multilayer perceptron (MLP) neural networks models, and adaptive neuro-fuzzy inference system (ANFIS) were applied to the data in order to predict the heavy metal concentrations. As a result of the study, the RMSE and MAE values of all the heavy metal models were found to have very low error values during the test phase, and it was found that the models created using MLP had R2 values higher than 0.77 during the test phase; the test phase R2 values of the models using RBN method were found to be ranging between 0.773 and 0.989, and the test phase R2 value of the ANFIS model was higher than 0.80. If sorted from the best model to the worst by taking the MAE and RMSE values into consideration based on the test evaluation results, according to the heavy metal types, where all of the MLP, RBN, and ANFIS models were generally approximate to each other, RBN was successful for Cu, Zn, and Mn, while MLP model was successful for Ni and ANFIS model for Fe and Pb. According to the results, it can be inferred that the heavy metal contents can be estimated approximately with artificial intelligence models and relatively easy-to-measure parameters; it will be possible to detect heavy metals which are harmful to the viability of the rivers, both quickly and economically. Keywords ANN . River . ANFIS model . Heavy metal . Contamination . Bartin River
Introduction Today, water streams such as rivers are subject to intensive pollution due to the growth of industrial activities and the Responsible Editor: Marcus Schulz * Mehmet Cetin [email protected] 1
Faculty of Engineering, Architecture and Design, Department of Environmental Engineering, Bartin University, Bartin, Turkey
2
Faculty of Engineering, Architecture and Design, Department of Civil Engineering, Bartin University, Bartin, Turkey
3
Faculty of Forestry, Department of Forest Engineering, Bartin University, Bartin, Turkey
4
Faculty of Engineering and Architecture, Department of Landscape Architecture, Kastamonu University, Kastamonu, Turkey
5
Faculty of Engineering and Architecture, Department of Environmental Engineering, Kastamonu University, Kastamonu, Turkey
rapid increase in population. Chemical pollution of rivers in particular is one of the biggest threats to human health and aquatic ecosystems (Magdaleno et al. 2014). Heav
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