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Data prediction of soil heavy metal content by deep composite model

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

The content of heavy metals in the soil is directly related to the control of soil pollution, but due to the limitations of manpower and material resources, it is difficult to detect them in detail; researchers usually need to predict the content of soil heavy metals in unknown areas based on existing data. Therefore, how to choose an effective method to complete this process has become a challenging problem.

Materials and methods

In this paper, a deep composite model (DCM) is proposed. The model is based on radial basis function neural network (RBFNN), then, uses self-adaptive learning based particle swarm optimization algorithm (SLPSO) to generate the weight and bias of the output layer of RBFNN and employs adaptive adjustment based root mean square back-propagation (ARMSProp) to optimize all variables of RBFNN, so as to improve the prediction accuracy of the model for soil heavy metal content. When using this model to predict soil heavy metal content, the Pearson coefficient is used as a comparison index to compare the correlation between different heavy metals and heavy metals to be predicted, and finally the content of heavy metals with a Pearson coefficient greater than 0.5 is selected as the input of the model variable.

Results and discussion

First in the validation of the proposed SLPSO algorithm, the effectiveness of SLPSO and the feasibility of being applied to the DCM model have been proved. Then, the DCM was applied to the prediction of soil heavy metal content in six new urban areas of Wuhan in China, the experimental results show that the predicted value of soil heavy metal content of DCM is closer to the actual value than other comparison models, and the four error indicator values of DCM are also significantly lower than other comparison models, especially when compared with RBFNN, the MAPE and SMAPE of DCM have dropped by 8.6% and 3.9%, respectively.

Conclusions

We can conclude that the deep composite model proposed in this paper obtains a good prediction accuracy when predicting soil heavy metal content; it has certain feasibility and can be used as an effective method for soil heavy metal content prediction.

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Funding

This work is supported by National Nature Science Foundation of China (Grant No.61272278); Major Technical Innovation Projects of Hubei Province (ID:2018ABA099); Natural Science Foundation of Hubei Province of China (Grant No.2015CFA061); National Science Fund for Youth of Hubei Province of China (Grant No.2018CFB408).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China

    Wenqi Cao & Cong Zhang

Authors
  1. Wenqi Cao
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  2. Cong Zhang
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Corresponding author

Correspondence to Cong Zhang.

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Responsible editor: Jun Zhou

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Cao, W., Zhang, C. Data prediction of soil heavy metal content by deep composite model. J Soils Sediments 21, 487–498 (2021). https://doi.org/10.1007/s11368-020-02793-y

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  • DOI: https://doi.org/10.1007/s11368-020-02793-y

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