BAYESIAN OPTIMIZATION OF A DYNAMIC VEGETATION MODEL
In this thesis, we apply Bayesian Optimization to estimate the parameters of the LPJ-GUESS dynamic vegetation model with a focus on Methane (CH4) emissions in the Siikaneva wetland in Finland. Previous research has used other statistical methods, such as Markov Chain Monte Carlo (MCMC), to estimate the LPJ-GUESS model parameters, achieving a root mean square error (RMSE) of 0.023. Our objective is