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Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Author

Listed:
  • Yun Yang

    (College of Geological Engineering and Geomatics, Chang’an University, No.126, Yanta Road, Xi’an 710054, China
    Key Laboratory of Formation Mechanism and Prevention and Control of Mine Geological Disasters, Ministry of Natural Resources, No.126, Yanta Road, Xi’an 710054, China)

  • Qinfang Cui

    (Piesat International Information Technology Co., Ltd., No.532, Shenzhou Third Road, Xi’an 710199, China)

  • Rongjie Cheng

    (College of Geological Engineering and Geomatics, Chang’an University, No.126, Yanta Road, Xi’an 710054, China)

  • Aidi Huo

    (School of Water and Environment, Chang’an University, No.126, Yanta Road, Xi’an 710054, China)

  • Yanting Wang

    (College of Geological Engineering and Geomatics, Chang’an University, No.126, Yanta Road, Xi’an 710054, China)

Abstract

Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.

Suggested Citation

  • Yun Yang & Qinfang Cui & Rongjie Cheng & Aidi Huo & Yanting Wang, 2023. "Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning," Sustainability, MDPI, vol. 15(15), pages 1-14, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11765-:d:1207003
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    References listed on IDEAS

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    1. Aidi Huo & Xing Wang & Zhixin Zhao & Luying Yang & Fangqian Zhong & Chunli Zheng & Ningbo Gao, 2022. "Risk Assessment of Heavy Metal Pollution in Farmland Soils at the Northern Foot of the Qinling Mountains, China," IJERPH, MDPI, vol. 19(22), pages 1-12, November.
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