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Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Author

Listed:
  • Chao Liu

    (School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Han Li

    (School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Jiuzhe Xu

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Weijun Gao

    (Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

  • Xiang Shen

    (Department of Statistic, George Washington University, Washington, DC 20052, USA)

  • Sheng Miao

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
    Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

Abstract

The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

Suggested Citation

  • Chao Liu & Han Li & Jiuzhe Xu & Weijun Gao & Xiang Shen & Sheng Miao, 2023. "Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas," IJERPH, MDPI, vol. 20(3), pages 1-21, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2513-:d:1052389
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    References listed on IDEAS

    as
    1. Hyun-Jun Choi & Sewon Kim & YoungSeok Kim & Jongmuk Won, 2022. "Predicting Frost Depth of Soils in South Korea Using Machine Learning Techniques," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    2. Taoyan Dai & Liquan Wang & Tienan Li & Pengpeng Qiu & Jun Wang, 2022. "Study on the Characteristics of Soil Erosion in the Black Soil Area of Northeast China under Natural Rainfall Conditions: The Case of Sunjiagou Small Watershed," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    3. Yao Shunyu & Nazir Ahmed Bazai & Tang Jinbo & Jiang Hu & Yi Shujian & Zou Qiang & Tashfain Ahmed & Guo Jian, 2022. "Dynamic process of a typical slope debris flow: a case study of the wujia gully, Zengda, Sichuan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 565-586, May.
    4. Zhengyang Li & Yafeng Lu & Yukuan Wang & Jia Liu, 2022. "The Spatio-Temporal Evolution of the Soil Conservation Function of Ecosystems in the North–South Transition Zone in China: A Case Study of the Qinling-Daba Mountains," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    5. Yi Wang & Wei He & Ting Zhang & Yani Zhang & Longxi Cao, 2022. "Adapting the WEPP Hillslope Model and the TLS Technology to Predict Unpaved Road Soil Erosion," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
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