Predicting coagulation–flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were applied for modeling and predicting turbidity removal from water using graphene oxide (GO). Based on partial...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 29; H. 48; S. 72839 - 72852
Hauptverfasser:	Ghasemi, Mahdi, Hasani Zonoozi, Maryam, Rezania, Nazila, Saadatpour, Motahareh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022 Springer Nature B.V
Schlagworte:	Adaptive systems Algorithms Aquatic Pollution Artificial intelligence Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Coagulation Comparative studies comparative study Data points Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental science Flocculation Fuzzy logic Graphene graphene oxide Mathematical models Model accuracy Modelling Neural networks Parameters Performance prediction prediction Predictions regression analysis Research Article Response surface methodology Root-mean-square errors Statistical analysis Statistics Support vector machines Turbidity Waste Water Technology Water Management Water Pollution Control ANN Coagulation–flocculation process Graphene oxide (GO) ANFIS RSM SVR
ISSN:	0944-1344, 1614-7499, 1614-7499
Online-Zugang:	Volltext
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