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Convolutional neural network-based applied research on the enrichment of heavy metals in the soil–rice system in China

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Abstract

The enrichment of heavy metals in the soil–rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R2 values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil–rice system and provided a new perspective and solution for heavy metal prediction.

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Data Availability

The data that support the findings of this study are available from “Risk assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and villages (Changsha)”, but restrictions apply to the availability of these data. The data were used under license for the current study, so they are not publicly available. However, data are available from the authors upon reasonable request and with permission of “Risk assessment Laboratory for Environmental Factors of Agro-product Quality Safety, Ministry of Agriculture and villages (Changsha)”.

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Acknowledgements

The authors are extremely thankful to the anonymous reviewers that work in this paper.

Funding

Part of this study was funded by the National Agricultural Product Quality and Safety Risk Assessment Project, China (GJFP201701201).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410005, People’s Republic of China

    Panpan Li, Xiaoguang Mao & Jianjun Xu

  2. College of Resources and Environment, Hunan Agricultural University, Changsha, 410128, People’s Republic of China

    Huijuan Hao & Dabing Ge

  3. Risk Assessment Laboratory for Environmental Factors of Agro-Product Quality Safety, Ministry of Agriculture and Villages, Changsha, 410005, People’s Republic of China

    Huijuan Hao, Yuntao Lv & Wanming Chen

  4. College of Information and Communication Technology, Guangzhou College of Commerce, Guangzhou, 510000, People’s Republic of China

    Zhuo Zhang

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  1. Panpan Li
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  2. Huijuan Hao
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Contributions

Panpan Li was involved in the conceptualization, methodology, software, data processing and analysis, visualization, and paper writing. Huijuan Hao contributed to the conceptualization, methodology, data collection, paper writing and editing. Xiaoguang Mao was involved in the conceptualization and methodology. Jianjun Xu contributed to the software and data processing. Yuntao Lv contributed to the resources and supervision. Wanming Chen helped in the sample collection, laboratory experiment, and quality control. Dabing Ge was involved in the paper editing and English polishing. Zhuo Zhang contributed to the supervision, organization, and paper reviewing. All authors read and approved the final manuscript.

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Li, P., Hao, H., Mao, X. et al. Convolutional neural network-based applied research on the enrichment of heavy metals in the soil–rice system in China. Environ Sci Pollut Res 29, 53642–53655 (2022). https://doi.org/10.1007/s11356-022-19640-x

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