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Prediction method of soil heavy metal content based on elman neural network model

A neural network model and neural network technology are applied in the field of soil heavy metal content prediction based on Elman neural network model, which can solve the problems of not considering the migration habit of heavy metals, difficult to accurately predict soil heavy metal content, etc., and achieve the effect of ensuring accuracy

Active Publication Date: 2020-06-02
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0006] The purpose of the present invention is to solve the defect that the migration habits of heavy metals in soil are not considered in the prior art so that it is difficult to accurately predict the content of heavy metals in soil, and a method for predicting the content of heavy metals in soil based on the Elman neural network model is provided to solve the above problems

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  • Prediction method of soil heavy metal content based on elman neural network model
  • Prediction method of soil heavy metal content based on elman neural network model
  • Prediction method of soil heavy metal content based on elman neural network model

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[0058] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and drawings are used in conjunction with detailed descriptions, and the description is as follows:

[0059] Such as figure 1 As shown, the soil heavy metal content prediction method based on the Elman neural network model of the present invention includes the following steps:

[0060] The first step is the acquisition and preprocessing of sample data. The collected soil samples are divided into training samples and test samples, and the spectral data of the soil in the training samples are obtained by LIBS technology to form training data. Use traditional chemical methods to detect and determine the heavy metal content of the training data, classify it as labeled data, and divide the training data into labeled data and unlabeled data.

[0061] After the collected soil has gone through the processes of impurity re...

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Abstract

The invention relates to a soil heavy metal content prediction method based on an Elman neural network model. Compared with the prior art, it solves the defect that it is difficult to accurately predict the soil heavy metal content because the migration habit of heavy metals in soil is not considered. The invention comprises the following steps: acquisition and preprocessing of sample data; constructing an Elman neural network predictive analysis model based on a stack autoencoder network; performing unsupervised training on the Elman neural network predictive analysis model based on a stack autoencoder network; Network Elman neural network predictive analysis model for supervised training; predictive analysis of soil heavy metal content. The invention uses the Elman neural network model improved by fully considering the migration characteristics of the heavy metal content in the soil to analyze and predict, thereby ensuring the prediction accuracy of the heavy metal elements in the soil.

Description

Technical field [0001] The invention relates to the technical field of soil data analysis, in particular to a soil heavy metal content prediction method based on an Elman neural network model. Background technique [0002] The pollution of heavy metals in soil has become a major agricultural ecological environmental problem that has been widely concerned by our country, and it has posed a serious threat to the sustainable development of modern agriculture and social economy, agricultural ecological environmental safety and agricultural product quality safety. More than ten years of scientific research and a large amount of practice have proved that due to the particularity of my country's agricultural ecological environment, copying foreign technology and theories cannot effectively solve the major environmental and scientific problems faced by my country's agricultural sector, and it is difficult to effectively curb agricultural environmental pollution and increasing Intensified ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/02G06N3/04
CPCG06Q10/04G06Q50/02G06N3/048G06N3/044
Inventor 王儒敬贾秀芳谢成军李伟鲁翠萍胡海瀛王雪
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI