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| International Agrophysics |
| wydawca: | Instytut Agrofizyki
im. B. Dobrzańskiego
PAN
w Lublinie |
| ISSN: |
0236-8722 |
vol. 26, nr. 2 (2012)
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poprzedni artykuł wróć do listy artykułów następny artykuł
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Prediction of soil physical properties by optimized support vector machines
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| (pobierz wersję PDF )
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A. Besalatpour1, M.A. Hajabbasi1, S. Ayoubi1, A. Gharipour2, A.Y. Jazi2
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1 Department of Soil Sciences, University of Technology, Isfahan, 84156-83111, Iran |
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2 Department of Mathematical Sciences, University of Technology, Isfahan, 84156-83111, Iran |
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vol. 26 (2012), nr. 2,
pp. 109-115
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streszczenie
The potential use of optimized support vector machines with simulated annealing algorithm in developing pre- diction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression pre- diction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiple- linear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.
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słowa kluczowe
soft computing, support vector machines, simulated annealing algorithm, soil shear strength, aggregate stability
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