Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1493–1510, 2021
https://doi.org/10.5194/gmd-14-1493-2021
Geosci. Model Dev., 14, 1493–1510, 2021
https://doi.org/10.5194/gmd-14-1493-2021

Model evaluation paper 16 Mar 2021

Model evaluation paper | 16 Mar 2021

Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China

Batunacun et al.

Data sets

XGBoost-SHAP values, prediction of grassland degradation Batunacun and R. Wieland https://doi.org/10.5281/zenodo.3937226

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Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.