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Wyszukujesz frazę "soil properties prediction" wg kryterium: Wszystkie pola


Wyświetlanie 1-2 z 2
Tytuł:
Information potential of the spectral response of Polish soils, in the NIR range, in the light of lucas database analyses. Soil properties vector model
Autorzy:
Gruszczyński, Stanisław
Powiązania:
https://bibliotekanauki.pl/articles/101552.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Stowarzyszenie Infrastruktura i Ekologia Terenów Wiejskich PAN
Tematy:
near infrared spectroscopy
soil properties prediction
machine learning model
Opis:
The paper presents simple machine learning models used for prediction of some soil properties based on the NIR spectral response. Data on mineral soils from Poland were taken from the LUCAS dataset. Machine learning model was used that is included in the category of so-called multilayer perceptron (MLP). The MLP model input was a vector of combined, transformed inputs made by means of the PLSR (partial last squares regression) algorithm (45 inputs in total). The output was a vector of properties, reduced to 9 components due to poor modelling effects of the P and K components. The estimation errors for texture, soil organic carbon (SOC) and carbonates can be considered acceptable from the point of view of their suitability in the development of cartographic documentation. It can be supposed that further regionalization will improve these results.
Źródło:
Infrastruktura i Ekologia Terenów Wiejskich; 2019, II/1; 95-104
1732-5587
Pojawia się w:
Infrastruktura i Ekologia Terenów Wiejskich
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of soil physical properties by optimized support vector machines
Autorzy:
Besalatpour, A.
Hajabbasi, M.A.
Ayoubi, S.
Gharipour, A.
Jazi, A.Y.
Powiązania:
https://bibliotekanauki.pl/articles/24338.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Agrofizyki PAN
Opis:
The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction 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 multiplelinear 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.
Źródło:
International Agrophysics; 2012, 26, 2
0236-8722
Pojawia się w:
International Agrophysics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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