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Multiple Regression

Multiple Regression

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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Related Experiment Video

Updated: Jul 17, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Predicting soil available cadmium by machine learning based on soil properties.

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

Summary

This summary is machine-generated.

Machine learning accurately predicts soil cadmium (Cd) availability, a key factor in food chain contamination. A post-constraint XGBoost model offers superior performance for assessing environmental risks and crop cadmium uptake.

Keywords:

Cadmium availability Machine learning Predictive modeling Soil properties

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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

Area of Science:

Environmental Science
Soil Science
Agricultural Science

Background:

Cadmium (Cd) accumulation in edible plants threatens human health via the food chain.
Accurate assessment of soil Cd availability is vital for environmental risk evaluation.
Traditional methods for soil Cd assessment are inefficient and time-consuming.

Purpose of the Study:

To develop and evaluate machine learning models for predicting soil available Cd.
To compare the performance of different machine learning models, including XGBoost and linear regression.
To investigate the relationship between soil available Cd and Cd accumulation in wheat and rice grains.

Main Methods:

Utilized a dataset of 585 soil samples to train and test machine learning models.
Developed and compared traditional linear regression models with a post-constraint eXtreme Gradient Boosting (XGBoost) model.
Employed linear regression to analyze the correlation between soil available Cd and grain Cd in wheat and rice.

Main Results:

The post-constraint XGBoost model achieved the highest predictive performance for soil available Cd (R² = 0.81), outperforming traditional linear regression.
Linear regression models showed significant correlations between soil available Cd and wheat grain Cd (R² = 0.487) and rice grain Cd (R² = 0.43).
Identified XGBoost as a robust tool for predicting soil Cd availability, addressing limitations of traditional methods.

Conclusions:

Machine learning, particularly the XGBoost model, provides an efficient and accurate approach for predicting soil available Cd.
The study confirms the strong link between soil Cd availability and Cd accumulation in staple crops like wheat and rice.
Findings support the use of advanced modeling for better environmental risk assessment and food safety management regarding cadmium contamination.