Direct measurement of cation exchange capacity and bulk density parameters is hard and costly; henceforth indirect methods of pedotransfer functions for predicting soil parameters are developed based on easily obtained parameters. In this regard, for estimating the cation exchange capacity and bulk density 63 samples were taken from 15 profiles in FASHAND zone in TEHRAN. Using pedotransfer function and having cation exchange capacity and bulk density as output variables, variablesthe normality of the input data were controlled by MINITAB software based on KOLOMOROF-SMIRNOV normality test. For developing the pedotransfer functions multiple regression and artificial neural network methods were enaged. Determining test and training data and then finding the best type of neural network, a hidden layer with 2 to 10 neurons was selected for an optimum simulation in terms of RMSA, ME and AARE indices. Input data to the network for predicting cation exchange capacity involved clay, silt, sand and organic material in percent while for predicting bulk density clay, silt, sand, lime, saturation moisture and organic material in percent were selected. Using multiple regrsion techniques and some experimental equations like Breeuwsma and teammates (1986), Matrique and Bell (1991) andvanKeulen (1995) regression equations were developed. Because of nonlinear relationship between input and output variables, neural networks had better performance in comparison with the regression models which for cation exchange capacity and bulk density gave coefficient of determination as 0.89 and 0.74, respectively. Regarding pedotransfer functions employed for estimating cation exchange capacity Matrique et al. (1991) models with a coefficient determination of 0.24 gave the same response which in comparison with the Breeuwsma (1986) models of a determination coefficient of 0.06 is more acceptable.
Mehrabi gohari, E., & Taghizadeh Mehrjardi, R. (2014). Accuracy assessments of artificial neural network for prediction of some soil parameters in FASHAND zone. Watershed Management Research, 27(2), 3-11. doi: 10.22092/wmej.2014.106242
MLA
Elham Mehrabi gohari; Ruhollah Taghizadeh Mehrjardi. "Accuracy assessments of artificial neural network for prediction of some soil parameters in FASHAND zone". Watershed Management Research, 27, 2, 2014, 3-11. doi: 10.22092/wmej.2014.106242
HARVARD
Mehrabi gohari, E., Taghizadeh Mehrjardi, R. (2014). 'Accuracy assessments of artificial neural network for prediction of some soil parameters in FASHAND zone', Watershed Management Research, 27(2), pp. 3-11. doi: 10.22092/wmej.2014.106242
VANCOUVER
Mehrabi gohari, E., Taghizadeh Mehrjardi, R. Accuracy assessments of artificial neural network for prediction of some soil parameters in FASHAND zone. Watershed Management Research, 2014; 27(2): 3-11. doi: 10.22092/wmej.2014.106242
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