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相关概念视频

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

Updated: Jul 17, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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通过基于土壤特性的机器学习来预测土壤中可用的.

Jiawei Huang1, Guangping Fan2, Cun Liu3

  • 1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China.

Journal of hazardous materials
|August 28, 2023
PubMed
概括

机器学习准确地预测土壤中的 (Cd) 可用性,这是食物链污染的关键因素. 限制后的XGBoost模型为评估环境风险和作物吸收提供了卓越的性能.

关键词:
的可用性 的可用性机器学习是机器学习.预测建模的预测建模.土壤的特性 土壤的特性

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Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
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科学领域:

  • 环境科学 环境科学
  • 土壤科学 土壤科学
  • 农业科学 农业科学

背景情况:

  • 在食用植物中积累的 (Cd) 通过食物链威胁到人类的健康.
  • 准确评估土壤Cd可用性对于环境风险评估至关重要.
  • 传统的土壤Cd评估方法是低效和耗时的.

研究的目的:

  • 开发和评估用于预测土壤可用Cd.的机器学习模型.
  • 为了比较不同机器学习模型的性能,包括XGBoost和线性回归.
  • 研究土壤中可用的Cd与小麦和大米粒中的Cd积累之间的关系.

主要方法:

  • 利用585个土壤样本的数据集来训练和测试机器学习模型.
  • 开发并比较传统的线性回归模型与后约束的极端梯度增强 (XGBoost) 模型.
  • 采用线性回归来分析在小麦和大米中土壤可用Cd和谷物Cd之间的相关性.

主要成果:

  • 限制后的XGBoost模型实现了土壤可用Cd (R2 = 0.81) 的最高预测性能,超过了传统的线性回归.
  • 线性回归模型显示了土壤可用Cd和小麦谷物Cd (R2 = 0.487) 和大米谷物Cd (R2 = 0.43) 之间的显著相关性.
  • 确定XGBoost作为预测土壤Cd可用性的强大工具,解决传统方法的局限性.

结论:

  • 机器学习,特别是XGBoost模型,提供了一种高效和准确的方法来预测土壤可用Cd.
  • 这项研究证实了土壤中Cd可用性与小麦和大米等主食作物中Cd积累之间的强烈联系.
  • 结果支持使用先进的建模来更好地评估环境风险和食品安全管理有关污染.