Soil phosphorus adsorption capacity evaluation method based on machine learning

By establishing a soil phosphorus adsorption model using machine learning methods, the problem of time-consuming and labor-intensive laboratory assessment in existing technologies has been solved, enabling rapid and accurate assessment of soil phosphorus adsorption capacity and supporting rational fertilization and water environment protection.

CN120873591APending Publication Date: 2025-10-31FUDAN UNIVERSITY
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Patent Information

Application Number
CN202510939381.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-08
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

Current technologies for assessing soil phosphorus adsorption capacity rely on time-consuming and resource-intensive laboratory batch equilibrium experiments, which cannot fully reflect regional soil properties and result in limited predictions of phosphorus migration and transformation processes.

Method used

Machine learning methods were employed to establish various machine learning models by acquiring multiple sets of soil medium adsorption model parameters and physicochemical property data, optimizing hyperparameters, and predicting soil phosphorus adsorption capacity, including Langmuir and Freundlich models. Outliers were removed and data were standardized, and the optimal model was used to evaluate the regional soil phosphorus adsorption capacity.

Benefits of technology

It improves assessment efficiency and accuracy, reduces detection costs, enables rapid prediction of regional soil phosphorus adsorption capacity, supports rational fertilization and reduces phosphorus loss, and promotes water pollution control.

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Abstract

The invention relates to the technical field of environmental science, and discloses a soil phosphorus adsorption capacity evaluation method based on machine learning, which comprises the following steps: S1, firstly obtaining adsorption model parameter estimation results of multiple groups of phosphorus in different types of soil media and main physicochemical property data of soil, S2, preprocessing the obtained data, S3, establishing multiple machine learning models, and S4, evaluating the phosphorus adsorption capacity of the soil according to the machine learning models. Based on main physicochemical properties of the soil, predicting main parameters of the soil phosphorus adsorption model, S4, selecting an optimal machine learning model; s5, the optimal machine learning model is used for evaluating the phosphorus adsorption capacity of the regional soil, a large amount of data can be rapidly processed by adopting the machine learning model, a prediction result is given, the evaluation efficiency is improved, a time-consuming phosphorus adsorption experiment does not need to be conducted on each new soil sample, and therefore the sample testing cost is reduced, and the method is suitable for popularization and application. The adsorption parameters can be quickly predicted through the trained model only by measuring key physicochemical properties which are relatively easy to obtain, so that the detection speed is greatly improved.
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