摘要
近年来机器学习在土壤理化和生物学性质预测研究中的应用日益广泛且效果良好。本文从3个方面展开综述,包括:(1)典型机器学习类型和土壤性质预测中的常用机器学习算法(如随机森林、支持向量机、BP神经网络等);(2)土壤性质回归预测中常用的模型精度指标;(3)基于机器学习方法的土壤性质预测研究一般流程及近期研究案例。在土壤性质预测研究中,除可以通过集成方法集成多个模型以提高工作效率、追求模型高精度外,还应重视预测的不确定性分析,继续探索模型可解释性、泛性和鲁棒性的提高方法;在选择机器学习算法、进行模型比较时需考虑研究需要和研究内容,同时建议引入多种类型的预测变量以保证预测科学性。
Machine learning has been widely used in the prediction of soil physical,chemical and biological properties in recent years.We summarized the research advances in this area from the following three aspects:(1)Typical machine learning types and common machine learning algorithms in prediction of soil property,including random forest,support vector machine,and BP neural network;(2)Model accuracy indices commonly used in regression prediction of soil property;(3)The general process and recent research cases of soil property prediction using machine learning.In the future research of soil property prediction,we should pay attention to the uncertainty assessment of prediction and coupling multiple models through ensemble methods,which would help improve work efficiency and pursue high accuracy of models.It is necessary to explore ways to improve the interpretability,generalization and robustness of models.When selecting machine learning algorithms and comparing models,it is recommended to use multiple types of predictor variables to ensure the accuracy of prediction after consideration of research needs and content.
作者
仇皓雷
王海燕
QIU Haolei;WANG Haiyan(Key Laboratory for Silviculture and Conservation of Ministry of Education,College of Forestry,Beijing Forestry University,Beijing 100083,China)
出处
《生态学杂志》
北大核心
2025年第1期283-294,共12页
Chinese Journal of Ecology
基金
国家科技基础资源调查专项基金(2021FY100801)资助。
关键词
土壤性质
机器学习
集成学习
深度学习
数字土壤制图
土壤健康
soil property
machine learning
ensemble learning
deep learning
digital soil mapping
soil health
作者简介
通信作者:王海燕,E-mail:Wanghaiyan72@bjfu.edu.cn;仇皓雷,男,2000年生,硕士研究生,主要研究方向为数字土壤制图。E-mail:hlboy233@163.com。
