Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1493–1510, 2021
Geosci. Model Dev., 14, 1493–1510, 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.

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Cited articles

Abdullah, A. Y. M., Masrur, A., Adnan, M. S. G., Baky, Md. A. A., Hassan, Q. K., and Dewan, A.: Spatio-temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017, Remote Sens., 11, 790,, 2019. 
Aburas, M. M., Ahamad, M. S. S., and Omar, N. Q.: Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review, Environ. Monit. Assess., 191,, 2019. 
Abu-Rmileh, A.: Be careful when interpreting your features importance in XGBoost!, Data Sci., available at:, last access: 14 June 2019. 
Ahmadlou, M., Delavar, M. R., and Tayyebi, A.: Comparing ANN and CART to Model Multiple Land Use Changes: A Case Study of Sari and Ghaem-Shahr Cities in Iran, J. Geomat. Sci. Technol., 6, 292–303, 2016. 
Ahmadlou, M., Delavar, M. R., Basiri, A., and Karimi, M.: A Comparative Study of Machine Learning Techniques to Simulate Land Use Changes, J. Indian Soc. Remote Sens., 47, 53–62,, 2019. 
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.