ArticleEnvironmental science and pollution research international2020
Application of artificial neural networks to predict the heavy metal contamination in the Bartin River.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Prediction of COD in industrial wastewater treatment plant using an artificial neural network.Scientific reports · 2024Article
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- Quantification of toxic metals in chicken egg and chicken feed via SOM-artificial neural network.Environmental monitoring and assessment · 2024Article
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- Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms.Environmental science and pollution research international · 2022Article
- Periodical and regional change of particulate matter and COEnvironmental monitoring and assessment · 2021Article
- Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria.Environmental science and pollution research international · 2021Article
- The usability of Cupressus arizonica annual rings in monitoring the changes in heavy metal concentration in air.Environmental science and pollution research international · 2021Article
- Using Cedrus atlantica's annual rings as a biomonitor in observing the changes of Ni and Co concentrations in the atmosphere.Environmental science and pollution research international · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 R
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