基于特征集成学习的四川省土壤厚度预测

Spatial Prediction of Soil Thicknesses in Sichuan Province Based on Feature-Ensemble Learning

以四川省土壤厚度预测为例,为农业生产与生态环境评价中土壤厚度空间分布图的编制提供方法支持。对比分析了随机森林、分位数回归森林、支持向量机、集成学习模型对连续型土壤厚度的预测精度,并提出了一种基于特征集成学习的土壤厚度类型预测算法。研究结果表明:①四川省土壤厚度具有较高的空间异质性,控制其空间变化的主要地形因子包括谷底平坦综合指数、高程与地形湿度指数;②四川省土壤厚度预测模型的决定系数为0.32~0.47,均方根误差为0.28~0.41 m;③面向连续型土壤厚度预测的集成模型具有较高的预测精度与稳健性,能够充分集成子模型的优势。特征集成学习能够有效集成并融合了连续型土壤厚度预测与离散型土壤厚度类型预测结果,通过减少方差来提高预测结果的稳健性。

This study compared the prediction accuracy of random forest,quantile regression forest,support vector machine and ensemble learning in mapping soil thickness taken as a continuous variable,where the machine learning models were weighted as individual models.Furthermore,a feature-ensemble learning algorithm was proposed for mapping soil thickness,in which soil thicknesses was classified as a new categorical variable,and the discrete predictions were further weighted with the predicted continuous soil thicknesses.The results showed that soil thicknesses in Sichuan Province were characterized with high spatial variation,of which the dominated drivers included multiresolution index of valley bottom flatness,elevation and topographic wetness index.The overall performance of prediction models in terms of coefficients of determinations and root mean square errors were 0.32–0.47 and 0.28–0.41 m,respectively.For the prediction of continuous soil thickness,ensemble models had low errors than those of individual models.For soil thickness types,the proposed feature-ensemble learning algorithm achieved higher robustness than other considered models by reducing the variance of prediction.