Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks
Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks
复制标题
通过人工神经网络利用土壤的一些力学特性估计土壤比表面积
DOI：
10.1007/s10661-018-6980-0
复制
发表时间：
2018
影响因子：
3
通讯作者：
R. Mohajer
中科院分区：
文献类型：
--
作者：
Leyla Jahanban;E. Ebrahimi;S. Moradi;M. Fallah;Leyla Geiratie Arani;R. Mohajer
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.
