View article

The effective prediction of corrosion inhibition efficiency (%IE) of modified graphene oxide (GO) derivatives diaminohexane-modified graphene oxide (DAH-GO) and diaminooctane-modified graphene oxide (DAO-GO) is vital for advanced material applications. This study employs a dual-modelling schema to predict the %IE, for this purpose, four stand-alone machine learning (ML) models (Multivariate Regression (MVR), Gaussian Process Regression (GPR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Neural Network (NN)), and five simple averaging (SA) ensemble paradigms (MVR-SA, GPR-SA, ANFIS-SA, NN-SA, and Decision Tree-SA (DT-SA)). Feature selection processes were carried out to develop three distinct models, leading to a comprehensive comparative analysis. The results demonstrate that the non-linear stand-alone models (GPR, ANFIS, NN) significantly outperform the linear MVR model …