H. Kashi; H. Ghorbani; S. Emamgholizadeh; S.A.A. Hashemi
Abstract
With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed ...
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With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity as difficult and time consuming feature. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of two artificial neural networks, multi-layer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compared with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity was determined and most important parameters which could influence the soil cation exchange capacity were described.
H. Ghorbani; A. Roohani; N. Hafezi Moghaddas
Abstract
In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means ...
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In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means comparision, variance and statistical distribution were performed between the observed points samples data and the estimated cadmium values to evaluate the performance of the pattern recognition method. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated cadmium concentrations. The results suggest that learning vector quantization (LVQ) neural network can learn cadmium cocentration model precisely. In addition the results also indicated that trained LVQ neural network had a high capability in predicting cadmium concentrations for non-sampled points. The technique showed that the LVQNN could predict and map the spatial cadmium concentrations variability. Our results indicated that it is possible to discriminate different cadmium levels in soil, using LVQNN.
