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Deep Learning for forest biomass carbon predictions

Deep learning additive models for simultaneous prediction of stand level above- and belowground biomass in tropical forests

Guidelines for applying the Executable Package: This package enables the application of the best-developed Deep Learning Additive Models (DLAMs) for simultaneously predicting stand level above- and belowground biomass and total (AGB, BGB and ABGB, respectively), while ensuring additivity in tropical forests.

Based on the article: “Comparing statistical and deep learning approaches for simultaneous prediction of stand level above- and belowground biomass in tropical forests” published in …, for more detailed information, please consult this reference.

  1. Please download the folder named: “DLAMs.rar” then save and extract it to your computer.
  2. Select the best model by choosing the subfolder: either “DLAMs 1” or “DLAM 9” or “DLAMs 10”.
    • The DLAM 1 subfolder contains the best DLAMs 1, which includes optimal seven predictive covariates (stand basal area (G), stand volume (V), mean annual temperature (T), elevation (EL), forest type (FT), average height (Hg), and soil group (SG)).
    • The DLAM 9 subfolder contains the DLAMs 9, which has only one predictor: stand basal area (G).
    • The DLAM 10 subfolder contains the DLAMs 10, which has only one predictor: stand volume (V).
  3. Enter your new predictor values into the file named ‘predictors data.csv’ located in this subfolder.
    • Units of numeric variables: G (m2/ha), V (m3/ha), T (oC), EL (m), Hg (m)
    • Using acronyms for nominal categorical variables: FT (DDF for Dry Dipterocarp Forest or EBLF for Evergreen Broadleaf Forest), and SG (FA for Ferric Acrisols, OA for Orthic Acrisols, and RF for Rhodic Ferrasols).
  4. Double-click on the file named ‘prediction.exe’ in this subfolder to start the prediction process. Please allow a few minutes for the program to complete its process.
  5. As a result, a new file named ‘prediction.csv’ will be created in this subfolder, containing simultaneous predictions of stand-level aboveground biomass (AGB, tons/ha), belowground biomass (BGB, tons/ha), and total (ABGB, tons/ha), ensuring additivity.