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.
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.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
16 Mar 2021
Model evaluation paper |
| 16 Mar 2021
| 16 Mar 2021
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Department of Geography, Humboldt-Universität zu Berlin, Unter
den Linden 6, 10099 Berlin, Germany
Leibniz Centre for Agricultural Landscape Research (ZALF),
Eberswalder Straße 84, 15374 Müncheberg, Germany
Ralf Wieland
Leibniz Centre for Agricultural Landscape Research (ZALF),
Eberswalder Straße 84, 15374 Müncheberg, Germany
Tobia Lakes
Department of Geography, Humboldt-Universität zu Berlin, Unter
den Linden 6, 10099 Berlin, Germany
Integrative Research Institute on Transformations of
Human-Environment Systems, Humboldt-Universität zu Berlin,
Friedrichstraße 191, 10099 Berlin, Germany
Claas Nendel
Leibniz Centre for Agricultural Landscape Research (ZALF),
Eberswalder Straße 84, 15374 Müncheberg, Germany
Integrative Research Institute on Transformations of
Human-Environment Systems, Humboldt-Universität zu Berlin,
Friedrichstraße 191, 10099 Berlin, Germany
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Latest update: 13 Dec 2024
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.
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use...
