智慧农业导刊2025，Vol.5Issue(5)：41-45,5.DOI:10.20028/j.zhnydk.2025.05.010
县域随机森林数字土壤属性制图预处理优化方法
摘要
Abstract
Random forest is an important method for digital soil attribute mapping.In this paper,considering the data imbalance and multicollinearity of environmental variables,an optimization study is carried out in the pretreatment stage of random forest mapping method.This study took the pH prediction mapping of surface soil sample points in Dengzhou City,Henan Province in 2007 as an example.In view of the imbalance of pH data distribution,SMOGN algorithm was used to ensure that the pH prediction range was in line with the actual distribution.Aiming at the problem of multicollinearity of environmental variables,the mapping accuracy of methods such as dilation factor,principal component analysis and stepwise regression is compared and analyzed,and a method to eliminate multicollinearity is given.When data imbalance is taken into account and multicollinearity is eliminated,the average absolute error and root-mean-square error accuracy indicators of all sample points are improved.Soil pH have a wider range,and extreme pH can also be speculated.The method in this paper can effectively ensure that the distribution range of estimated pH is more in line with the actual situation,and improve the accuracy of pH estimation by the random forest method.关键词
数字土壤属性制图/土壤pH/随机森林/数据不平衡性/多重共线性Key words
digital soil attribute mapping/soil pH/random forest/data imbalance/multicollinearity分类
农业科技引用本文复制引用
王凤仪,赵东保,刘湃,肖炼..县域随机森林数字土壤属性制图预处理优化方法[J].智慧农业导刊,2025,5(5):41-45,5.基金项目
国家自然科学基金(41971346) （41971346）
四川省科技计划项目重点研发项目(2022YFN002) （2022YFN002）
