Izvorni znanstveni članak

https://doi.org/10.15255/KUI.2019.024

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

Yamina Ammi ; University Center Ahmed Zabana Relizane, 48 000, Algeria
Latifa Khaouane ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Salah Hanini ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria

Sažetak

A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN₁, QSAR-NN₂, and QSAR-NN₃ in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN₁, 980 data points for QSAR-NN₂, and 436 data points for QSAR-NN₃ were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN₁, 0.9338 for QSAR-NN₂, and 0.9709 for QSAR-NN₃). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN₁, 9.1991 % for QSAR-NN₂, and 5.8869 % for QSAR-NN₃, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR₁, 19.3815 % for QSAR-MLR₂, and 16.2062 % for QSAR-MLR₃).


This work is licensed under a Creative Commons Attribution 4.0 International License.

Ključne riječi

neural networks; multiple linear regressions; models; rejection; micropollutants; membranes

Hrčak ID:

235865

URI

https://hrcak.srce.hr/235865

Datum izdavanja:

19.3.2020.

Podaci na drugim jezicima: hrvatski

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