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

Autoren: Fatemeh Ghafarian, Ralf Wieland, Dietmar Lüttschwager, Claas Nendel

DAKIS | 10.2022 | DOI:
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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 networksrandom forestsupport 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.

Publikationsdatum: 10.2022

Verlag: Elsevier BV

Quelle: Environmental Modelling & Software | | 105466 | 156

Publikationstyp: Journal-Artikel