基于改进随机森林模型的多要素层次插值技术在土壤硒元素空间分布上的运用

Application of Multifactor Hierarchical Interpolation Technique Based on Improved Random Forest Model in Spatial Distribution of Selenium in Soils

地球化学元素的空间分布变异特征复杂,传统区域地球化学调查分析只考虑空间信息要素,无法反映其他要素层次异质性。因此提出采用地质要素、地理要素、空间要素、生态要素和人类活动等层次区域化标签作为机器学习模型的协变量进行硒元素空间分布预测。通过对比随机森林模型、极致梯度提升树模型、特征贡献—随机森林模型、深度神经网络模型以及传统的空间插值模型,发现特征贡献—随机森林模型在硒元素预测方面具有更高的精度,表明基于特征贡献—随机森林模型的多要素层次插值技术在土壤硒元素空间分布预测中具有较高的可行性。根据预测结果对硇洲岛硒元素空间分布进行贝叶斯局部空间自相关分析表明,影响硒元素空间分布的关键因子是基础地质条件和人类活动等。研究可进一步拓展地球化学数据的服务应用维度,使土壤地球化学数据的运用更加精确和科学。

The spatial distribution of geochemical elements is complex,and traditional regional geochemical surveys and analyses consider only spatial information elements,which do not reflect the heterogeneity of other elements.Therefore,this study used hierarchical regionalization labels,such as geological,geographical,spatial,and ecological elements along with anthropogenic activities,as covariates of the machine learning model to predict the spatial distribution of selenium elements.By comparing Random Forest(RF),extreme gradient lifting tree(XGboost),Feature Contribution-Random Forest(FC-RF),Deep learning Neural Network(DNN)models,and traditional spatial interpolation techniques;this study showed that the FC-RF model has higher precision in selenium prediction,which indicates that multilevel interpolation technology based on the FC-RF model is more feasible for predicting the spatial distribution of soil selenium.Based on the predicted results,a Bayesian Laser Interferometer Space Antenna(LISA)spatial correlation analysis was conducted on the spatial distribution of selenium on Naozhou Island,indicating that the key factors affecting the spatial distribution of selenium are basic geological conditions and anthropogenic activities.This study further expands the service application dimensions of geochemical data,thus making the use of soil geochemical data more accurate and scientific.