Method for predicting heavy metal concentration in soil colloid solution based on machine learning model

By using machine learning models, especially random forests and gradient boosting regression tree models, combined with soil colloid extraction parameters, the problem of predicting heavy metal concentration in soil colloidal solutions that is difficult to predict using traditional methods has been solved, and accurate prediction of heavy metal concentration has been achieved.

CN122449099APending Publication Date: 2026-07-24CHINA UNIV OF MINING & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF MINING & TECH
Filing Date
2026-06-02
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Traditional statistical analysis methods are insufficient to fully capture the complex nonlinear relationships in the process of heavy metal release mediated by soil colloids, and cannot effectively predict the concentration of heavy metals in soil colloidal solutions.

Method used

Machine learning models, especially random forest models, were used to construct a set of feature variables by combining experimental and property parameters extracted from soil colloids. Heavy metal concentrations were predicted using a random forest classifier and a gradient boosting regression tree model, and the XGBoost model was used for optimization.

Benefits of technology

It enables accurate prediction of heavy metal concentration in soil colloidal solutions, provides a reliable technical method, and improves the accuracy and reliability of prediction results.

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Abstract

The present application belongs to the technical field of environmental data analysis, and particularly relates to a method for predicting heavy metal concentration in soil colloid solution based on a machine learning model. Colloid extraction is performed on heavy metal contaminated soil samples, and characteristic variables of the soil colloid solution are determined. The characteristic variables are standardized and coded, and the preprocessed characteristic variables are respectively input into pre-trained machine learning models for predicting the concentrations of As, Cd, Pb and Zn in the soil colloid solution to obtain predicted values corresponding to the heavy metal concentrations. The pre-trained machine learning models for As and Pb are two-stage models, the first stage being a release probability classification model and the second stage being a release amount regression model. The pre-trained machine learning models for Cd and Zn are single-stage regression models. The present application provides a method for predicting heavy metal concentration in soil colloid solution based on a machine learning model, which provides a reliable technical method for accurately predicting heavy metal release characteristics.
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