Climate change is a major challenge to humankind. Solar desalination is a strategic option for overcoming water scarcity as a result of climate change. Modeling of solar still productivity (SSP) plays a significant role in the success of a solar desalination project to optimize capital expenditures and maximize production. A solar still was used to desalinate seawater in this study. An adaptive neuro-fuzzy inference system (ANFIS) for prediction of SSP was developed with different types of input membership functions (MFs). The investigation used the principal parameters affecting SSP, which are the solar radiation, relative humidity, total dissolved solids (TDS) of feed, TDS of brine, and feed flow rate. The performance of ANFIS models in the training, testing, and validation stages are compared with the observed data. The ANFIS model with Pi-shaped curve MF provides better and higher prediction accuracy than models with other MFs. The ANFIS was an adequate model for the prediction of SSP and yielded root mean square error and correlation coefficient values of 0.0041 L/m2/h and 99.99%, respectively. Sensitivity analysis revealed that solar radiation is the most effective parameter on SSP. Finally, the ANFIS model can be used effectively as a design tool for solar still systems.

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