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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Toxicology, Pollution and the Environment
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1440296

Developing an SSA-Optimized Attention-ConvGRU Model for Predicting and Assessing Soil Contaminant Distribution

Provisionally accepted
Yajie Liang Yajie Liang 1*Jieyu Zhao Jieyu Zhao 1Yiting Zhang Yiting Zhang 1Jisheng Li Jisheng Li 2Jieran Ding Jieran Ding 1Changyong Jing Changyong Jing 1Jiukun Ji Jiukun Ji 1Dongtan Wu Dongtan Wu 1
  • 1 Hebei University of Environmental Engineering, Qinhuangdao, China
  • 2 Other, Xi'an, China

The final, formatted version of the article will be published soon.

    Soil pollution, which includes a variety of contaminants such as heavy metals and organic compounds, poses significant environmental and health risks. Current predictive models often struggle with the complexity and diversity of soil contaminant behaviors, leading to limitations in their accuracy and applicability. To address these challenges, our study introduces a novel SSA-optimized Attention-ConvGRU model. This model integrates convolutional neural networks, gated recurrent units, and attention mechanisms, enhanced through optimization with the Sparrow Search Algorithm to improve predictive performance. Experimental results demonstrate that this model significantly outperforms traditional models. On the EPA dataset, the proposed model achieves a Mean Absolute Error (MAE) of 12.57 and a Root Mean Square Error (RMSE) of 20.74. These results highlight the superior performance and potential of the SSA-optimized Attention-ConvGRU model in predicting soil pollution, providing crucial support for environmental management and public health protection.

    Keywords: Soil Pollution, deep learning, sparrow search algorithm, ConvGRU, attention mechanisms, Attention-ConvGRU

    Received: 29 May 2024; Accepted: 15 Jul 2024.

    Copyright: © 2024 Liang, Zhao, Zhang, Li, Ding, Jing, Ji and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yajie Liang, Hebei University of Environmental Engineering, Qinhuangdao, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.