Solar still productivity (SSP) is of vital importance in solar desalination projects' planning and management. In this investigation, the applicability of adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR), in modeling SSP is investigated. Eight different membership functions (MFs) were used with ANFIS approach. Solar radiation, relative humidity, feed flow rate, total dissolved solids of feed, and brine are used as inputs to the models. The outcomes of the ANFIS are compared with those of the MNLR with respect to correlation coefficient (CC), root mean square error (RMSE), overall index of model performance (OI), and mean absolute error (MAE). Comparison results illustrate the generalized bell MF with ANFIS model has better accuracy than the other seven MFs in modeling SSP. Performance evaluation criteria show the predictive abilities of ANFIS and MNLR models were very similar and can be suggested to predict SSP effectively. Using the ANFIS model, the average value of CC, RMSE, OI, and MAE was 0.96, 0.05 L/m2/h, 0.91, and 0.04 L/m2/h, respectively. The corresponding values for the MNLR model were CC = 0.97, RMSE = 0.06 L/m2/h, OI = 0.93, and MAE = 0.05 L/m2/h. One of the advantages of MNLR model is using explicit equations.

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