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As decision-makers, researchers encounter highly dynamic, complex problems requiring suitable nature-based and industrial quantitative tools for performance analyses, syntheses, and modelling. This study employed a new-generation emotional artificial neural network (EANN) and other stand-alone approaches to model the performance of hybrid nanofiltration (NF) and reverse osmosis (RO) desalination plants in terms of permeate recovery (PR) (%) and permeate flow rate (PFR) (m³/h). With the obtained experimental data, augmented Dickey-Fuller (ADF) and Philip Perron tests, evaluated using the Akaike information criterion and Schwarz information criterion, were employed to assess stability and stationarity. Nonlinear input-variable feature selection based on mutual information and neuro-sensitivity analysis (NSA) was conducted on several input variables before modelling. The EANN was compared with …