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.

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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, https://doi.org/10.3390/rs11070790, 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, https://doi.org/10.1007/s10661-019-7330-6, 2019. 
Abu-Rmileh, A.: Be careful when interpreting your features importance in XGBoost!, Data Sci., available at: https://towardsdatascience.com/be-careful-when-interpreting-your-features-importance-in-xgboost-6e16132588e7, 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, https://doi.org/10.1007/s12524-018-0866-z, 2019. 
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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.