2017 NO.1
Predicting soil elements spatial distribution with two models
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| Predicting soil elements spatial distribution with two models | ||
| Zheng Jia | ||
| Guangdong Academy of Forestry | ||
| Abstract: | ||
| The BP artificial neural network and the spatial interpolations model (Universal Kriging, Spline method and Inverse Distance Weighted) were used to predict the forest soil elements (exchangeable calcium, sulfur and exchangeable magnesium) spatial distribution in Yuncheng district and Yun’an district in Yunfu city, Guangdong province. It was showed that the highest content element in the forestry soil was exchangeable Ca (of 17.333—1 169.033 mg·kg -1 ), followed by total S and exchangeable Mg with concentration of (60.787— 354.600 and 8.320—51.580 mg·kg -1 , respectively. The coefficient of variation for the three elements varied from 33.43%—106.34%. The variation coefficient of exchangeable Ca in Yun’an district reached 106.34%, indicating high variation in the concentration of ex- changeable Ca. Among three interpolation methods, deviation from the Universal Kriging was smaller than from other methods. But the Universal Kriging was un- able to predict other elements well in the three elements, except exchangeable Mg. By comparison, we can con- clude that the BP artificial neural network was the best model to predict the element spatial distribution in this study. | ||
| Key words: Forest soil · Elements · Spatial interpola- tion model · BP artificial neural network · Yunfu |