TY  - JOUR
A1  - Ghafarian, Fatemeh
A1  - Wieland, Ralf
A1  - Lüttschwager, Dietmar
A1  - Nendel, Claas
T1  - Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data
T2  - Environmental modelling & software with environment data news
N2  - 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.
KW  - cooling effect
KW  - machine learning
KW  - ensemble method
KW  - ecosystem services
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/62951
SN  - 1364-8152
SN  - 1873-6726
VL  - 156
PB  - Elsevier
CY  - Oxford
ER  -