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Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

Considering the important role of soil fertility and nutrient management in the modern agriculture seems to be a key step in appropriate site-specific fertilizers management for crop production. The present study was conducted to prepare a soil fertility zonation map based on soil nutrient elements including total nitrogen, available potassium and phosphorus, magnesium, manganese and iron and soil chemical parameters comprising cation exchange capacity, organic carbon, salinity and pH by integrated Fuzzy and AHP approaches for potato production in Rokh plain, northeast of Iran. In this regard the most important soil chemical parameters and nutrient elements in 0-30 cm depth of the soil was analyzed and mapped. The S-shaped fuzzy membership function was subsequently defined for each factor to fuzzify soil fertility parameters. The soil fertility map was prepared by weighing factor layers by the AHP approach and summation of factor layers by IDW interpolation function in GIS. The values of the soil fertility index in the scale of 0 to 1 ranged from 0.104 to 0.574, classified the study area in very low (922.90 km 2), low (566.10 km 2) and moderate fertility (14.86 km 2) classes which comprises 61.37%, 37.64% and 0.99% of the surface area, respectively. A regression between soil fertility values and potato yield in the study area revealed a high correlation (R 2 = 0.91) between the observed results which validate the zonation of the fertility classes in the region.

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