We explored the application of different feature mining methods combined with genera-lized boosted regression models in digital soil mapping. Environmental covariates were selected by two feature selection methods i.e., recursive feature elimination and selection by filtering. Using the original environmental covariates and the selected optimal variable combination as independent varia-bles, soil pH prediction model of Anhui Province was established and mapped based on the genera-lized boosted regression model and random forest model. The results showed that both kinds of feature mining methods could effectively improve the accuracy of soil pH prediction by generalized boosted regression models and random forest model, and could reduce dimensionality. Compared with the random forest model, the prediction accuracy of the validation set of the generalized boosted regression model was slightly lower. In the training set, the accuracy of the generalized boosted regression models was much higher than that of the random forest model, with higher interpretation and better overall effect. The main parameters of the random forest model, ntree and mtry, had limi-ted effect on the model. Different parameters and their combination could affect the prediction accuracy of the generalized boosted regression models, and thus should be tuned before modeling. The results of spatial mapping showed that soil pH in Anhui Province showed a pattern of "south acid and north alkali".
为探讨不同特征挖掘方法与广义提升回归模型相结合在数字土壤制图中的应用,本研究首先使用递归特征消除和过滤式两种特征筛选方法对环境协变量进行筛选,再分别使用原始环境协变量、筛选后的最优变量组合作为自变量,建立基于广义提升回归模型和随机森林模型的安徽省土壤pH预测模型并进行制图。结果表明: 引入两种特征挖掘方法均可有效提高广义提升回归模型和随机森林模型预测土壤pH的精度,并且可以起到降维的作用;相较于随机森林模型,广义提升回归模型的验证集预测精度略低,在训练集中,广义提升回归模型的精度却远高于随机森林模型,模型解释度高,整体效果较好;随机森林模型的主要参数ntree和mtry对于模型的影响程度较低,而不同参数对于广义提升回归模型的预测精度影响较大,不同参数组合模型精度不同,建模前需要进行调参。空间制图结果表明,安徽省土壤pH呈“南酸北碱”趋势。.
Keywords: Anhui Province; feature mining; generalized boosted regression models; machine learning; random forest; soil pH.