Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

被引:2
|
作者
Wang, Jiaxue [1 ,2 ]
Wei, Yujiao [1 ,2 ]
Sun, Zheng [1 ,2 ]
Gu, Shixiang [3 ]
Bai, Shihan [3 ]
Chen, Jinming [3 ]
Chen, Jing [3 ]
Hong, Yongsheng [4 ,5 ]
Chen, Yiyun [1 ,2 ]
机构
[1] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
[2] Wuhan Univ, Soil Survey & Monitoring Lab, Wuhan 430079, Peoples R China
[3] Yunnan Inst Water & Hydropower Engn Invest Design, Kunming 650021, Peoples R China
[4] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China
[5] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词
soil erodibility; environmental covariates; K models; soil erosion; ORGANIC-MATTER; EROSION; ECOSYSTEM; CHINA;
D O I
10.3390/rs16163017
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Optimal Mapping of Soil Erodibility Factor (K) Using Machine Learning Models in a Semi-arid Watershed
    Ghavami, Mohammad Sajjad
    Na, Zhou
    Ayoubi, Shamsollah
    Marandi, Salman Naimi
    Cerda, Artemi
    EARTH SYSTEMS AND ENVIRONMENT, 2025,
  • [2] Digital mapping of soil erodibility factor in northwestern Iran using machine learning models
    Kamal Khosravi Aqdam
    Farrokh Asadzadeh
    Hamid Reza Momtaz
    Naser Miran
    Ehsan Zare
    Environmental Monitoring and Assessment, 2022, 194
  • [3] Digital mapping of soil erodibility factor in northwestern Iran using machine learning models
    Aqdam, Kamal Khosravi
    Asadzadeh, Farrokh
    Momtaz, Hamid Reza
    Miran, Naser
    Zare, Ehsan
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2022, 194 (05)
  • [4] Mapping of erodibility and potential soil erosion in a hillside watershed
    Miguel, Pablo
    Diniz Dalmolin, Ricardo Simao
    Moura-Bueno, Jean Michel
    Soares, Mauricio Fornalski
    da Cunha, Henrique Noguez
    Albert, Renata Pinto
    Stumpf, Lizete
    Leidemer, Jeferson Diego
    ENGENHARIA SANITARIA E AMBIENTAL, 2021, 26 (01) : 1 - 9
  • [5] Rill erodibility as influenced by soil and land use in a small watershed of the Loess Plateau, China
    Li, Zhen-wei
    Zhang, Guang-hui
    Geng, Ren
    Wang, Hao
    BIOSYSTEMS ENGINEERING, 2015, 129 : 248 - 257
  • [6] Soil erodibility and its influencing factors on the Loess Plateau of China: a case study in the Ansai watershed
    Zhao, Wenwu
    Wei, Hui
    Jia, Lizhi
    Daryanto, Stefani
    Zhang, Xiao
    Liu, Yanxu
    SOLID EARTH, 2018, 9 (06) : 1507 - 1516
  • [7] Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale
    Ghavami, Mohammad Sajjad
    Ayoubi, Shamsollah
    Mosaddeghi, Mohammad Reza
    Naimi, Salman
    JOURNAL OF MOUNTAIN SCIENCE, 2023, 20 (10) : 2975 - 2992
  • [8] Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale
    Mohammad Sajjad Ghavami
    Shamsollah Ayoubi
    Mohammad Reza Mosaddeghi
    Salman Naimi
    Journal of Mountain Science, 2023, 20 : 2975 - 2992
  • [9] Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale
    Mohammad Sajjad GHAVAMI
    Shamsollah AYOUBI
    Mohammad Reza MOSADDEGHI
    Salman Naimi
    JournalofMountainScience, 2023, 20 (10) : 2975 - 2992
  • [10] Variation in soil erodibility under five typical land uses in a small watershed on the Loess Plateau, China
    Wang, Hao
    Zhang, Guang-hui
    Li, Ning-ning
    Zhang, Bao-jun
    Yang, Han-yue
    CATENA, 2019, 174 : 24 - 35