Using appropriate models in the land use planning process will help increase the accuracy and precision of decisions made by designers. The aim of this study was to investigate and compare fuzzy-based models (fuzzy set theory, fuzzy-AHP, and fuzzy-ANP) to evaluate the suitability of cotton cultivation in Sarayan region (located in eastern Iran). Twenty-eight land units were selected. Weighted arithmetic means of characteristics were performed in representative soil profiles of each unit. Landform-related characteristics were directly entered into the land suitability evaluation modeling. The land index was calculated using three selective qualitative land suitability model guidelines. Qualitative and quantitative land suitability was estimated. The validity of models was determined by r(2), RMSE, GMER, and MAPE indicators between predicted and actual production. Soil texture, pH, calcium carbonate equivalent, drainage, organic matter, salinity and sodicity, slope, and gypsum are the most important, respectively. Also, the fuzzy-ANP method is more efficient than other models due to its higher r(2) (0.98) and lower RMSE (4.31) and MAPE (0.56) and GMER (0.99) closer to 1. The value of cotton production using fuzzy, fuzzy-AHP, and fuzzy-ANP methods was calculated as 1085 to 4235, 1235 to 4318, and 1391 to 4452 tons per hectare, respectively. The high efficiency of the fuzzy-ANP model is due to the characteristics of the lands used in the evaluation process that are not independent of each other and this model considers them. Examining these models in different weather conditions and combining with the other computational intelligence methods in future experiments are recommended.
