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

Batunacun,; Wieland, Ralf; Lakes, Tobia; Nendel, Claas

doi:https://doi.org/10.5194/gmd-14-1493-2021

Articles | Volume 14, issue 3

https://doi.org/10.5194/gmd-14-1493-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-1493-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 3

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, Ralf Wieland, Tobia Lakes, and Claas Nendel

Supplement

https://doi.org/10.5194/gmd-14-1493-2021-supplement

Data sets

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

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.