Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

Performance improvement of modified tubular solar still by employing vertical and inclined pin fins and external condenser: an experimental study

Environmental Science and Pollution Research - The current experimental work aims to improve an accumulative yield of tubular solar distillers. This was achieved by utilizing the pin fins and...     

Spatial analysis of chromium in southwestern part of Iran: probabilistic health risk and multivariate global sensitivity analysis

This study was concerned with chromium as a potential carcinogenic contaminant in 64 wells located in five aquifers, southwest of Iran. A probabilistic health risk assessment indicated a high risk to the local residents including adults and children in the study area. A sequential sensitivity analysis and a novel approach known as multivariate global sensitivity analysis using both principal component analysis and B-spline were applied to investigate the behavior of health risk model along time considering four independent input parameters in the risk equation. In this context, based on the results of sensitivity analysis, concentration of chromium in drinking water (C_w) and body weight (W) were the most influential parameters. Random forest (RF) was used as a variable selection method to choose the most influential parameters for the prediction of chromium. Five parameters, among 13 water quality variables, including phosphate, nitrate, fluoride, manganese and iron were selected by RF as the most important parameters for spatial prediction. Hybrid methods of RF and ordinary kriging (RFOK) and RF and inverse distance weighting (RFIDW) were then applied for spatial prediction of Cr using the secondary variables. The RFOK and RFIDW were more efficient than that of ordinary kriging (OK) with respect to a cross-validation algorithm. For instance, in terms of relative root mean squared error, the performance of OK was improved from 31.72 to 23.21 and 23.61 for RFOK and RFIDW, respectively.

     

Antitumor effect of copper nanoparticles on human breast and colon malignancies

Environmental Science and Pollution Research - Breast and colon carcinomas are two types of common cancers which lead to cancer-related deaths. Due to their cytotoxic potential against cancer...     

Numerical and artificial intelligence models for predicting the water advance in border irrigation

Environment, Development and Sustainability - The water advance time (Ta) is needed for designing and evaluating surface irrigation systems. This study employed artificial neural networks (ANNs)...