Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data

• DOI: 10.1016/j.envsoft.2022.105466
• Publication Date: 2022-07-31T09:29:44Z
• Authors: Fatemeh Ghafarian; Ralf Wieland; Dietmar Lüttschwager; Claas Nendel
• Keywords: ddc:500; Ensemble method ; Cooling effect ; Machine learning ; Ecosystem services; 13. Climate action; Machine learning; 0207 environmental engineering; Ecosystem services; 02 engineering and technology; Mathematisch-Naturwissenschaftliche Fakultät; 15. Life on land; Ensemble method; Institut für Biochemie und Biologie; Cooling effect

Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting a Machine Learning technique to predict the microclimate of forest sites in Brandenburg, Germany, using seasonal data comprising weather features. The analysis was amended by applying a SHapley Additive explanation to show the interaction effect of variables and individualised feature attributions. We evaluate model performance in comparison to artificial neural networks, random forest, support vector machine, and multi-linear regression. After implementing a feature selection, an ensemble approach was applied to combine individual models for each forest and improve robustness over a given single prediction model. The resulting model can be applied to translate climate change scenarios into temperatures inside forests to assess temperature-related ecosystem services provided by forests.