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Showing 1 - 20 results of 6,112 for search '(((( forest modeling algorithm ) OR ( element method algorithm ))) OR ( level using algorithm ))', query time: 0.38s Refine Results
  1. 1
    Example measurement and analysis units for landscape elements.

    Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

    Published 2025
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  2. 2
    Description of the dataset used for the machine learning algorithm.

    Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

    Published 2024
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  3. 3
    Boxplot with result of evaluation of distribution modelling algorithms using the area under curve (AUC) method.

    Boxplot with result of evaluation of distribution modelling algorithms using the area under curve (AUC) method. by Rafael Pereira (6635264)

    Published 2025
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  4. 4
    Feature selection results from the Boruta algorithm.

    Feature selection results from the Boruta algorithm. by Junying Wei (1710550)

    Published 2025
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  5. 5
    Framework of the machine learning algorithm for single-family housing price.

    Framework of the machine learning algorithm for single-family housing price. by Jaewon Han (8660295)

    Published 2025
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  6. 6
    Model classification evaluation indicators under various machine algorithms.

    Model classification evaluation indicators under various machine algorithms. by Jinlin Teng (17972595)

    Published 2024
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  7. 7
    Features selection using the Boruta algorithm.

    Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

    Published 2025
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  8. 8
    Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  9. 9
    Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx

    Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  10. 10
    Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  11. 11
    Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  12. 12
    Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  13. 13
    Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  14. 14
    Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  15. 15
    Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  16. 16
    Image 5_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 5_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
    Save to List
    Saved in:
  17. 17
    Image 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
    Save to List
    Saved in:
  18. 18
    Table 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx

    Table 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
    Save to List
    Saved in:
  19. 19
    Image 7_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 7_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  20. 20
    TreeMap 2016 Forest Type Algorithm (Image Service)

    TreeMap 2016 Forest Type Algorithm (Image Service) by U.S. Forest Service (17476914)

    Published 2024
    “…<div>TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.…”
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