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Иш: Predicting BOD5 Removal Efficiency in a Constructed Wetland from Satellite and Meteorological Data Using Interpretable Machine Learning

  1. Random Forests

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    ABI
  2. XGBoost

    Tianqi Chen, Carlos Guestrin

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    ABI
  3. Red and photographic infrared linear combinations for monitoring vegetation

    Compton J. Tucker

    Мақола197922 иқтибос
    ABI
  4. Greedy function approximation: A gradient boosting machine.

    Jerome H. Friedman

    Мақола200114 иқтибос
    ABI
  5. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

    Carsten F. Dormann, Jane Elith, Sven Bacher +15

    Шарҳ мақола201212 иқтибос
    ABI
  6. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

    Bo‐Cai Gao

    Мақола199611 иқтибос
    ABI
  7. From local explanations to global understanding with explainable AI for trees

    Scott Lundberg, Gabriel Erion, Hugh Chen +7

    Мақола202011 иқтибос
    ABI
  8. Regression Shrinkage and Selection Via the Lasso

    Robert Tibshirani

    Мақола199610 иқтибос
    ABI
  9. Extremely randomized trees

    Pierre Geurts, Damien Ernst, Louis Wehenkel

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    ABI
  10. Regularization and Variable Selection Via the Elastic Net

    Hui Zou, Trevor Hastie

    Мақола20056 иқтибос
    ABI
  11. A tutorial on support vector regression

    Alex Smola, Bernhard Schölkopf

    Мақола20046 иқтибос
    ABI
  12. Ridge Regression: Biased Estimation for Nonorthogonal Problems

    Arthur E. Hoerl, Robert W. Kennard

    Мақола19705 иқтибос
    ABI
  13. PLS-regression: a basic tool of chemometrics

    Svante Wold, Michael Sjöstróm, Lennart Eriksson

    Мақола20015 иқтибос
    ABI
  14. Сарлавҳасиз

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  15. Land surface temperature retrieval from LANDSAT TM 5

    José A. Sobrino, Juan C. Jiménez‐Muñoz, Leonardo Paolini

    Мақола20043 иқтибос
    ABI
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