On auto-calibration algorithms for a forest growth simulation model

Abstract

Forest growth simulation models are useful in evaluating the effects of management practices and climate changes in terrestrial ecosystems, however their successful application requires accurate calibration of model parameters. We have implemented here a stepwise line search (SLS), Gibbs sampling (GS) and preclustering based strength Pareto algorithm (K-SPEA2) to find an optimal set of parameters.

Integrated Simulation and Calibration Framework for Heating System Optimization

“…Applications include the automatic calibration of traffic simulations [5][6][7], where parametrized driving behavior is tuned to fit road utilization, the calibration of building energy consumption simulations [8,9], where energy consumption predictions are compared against monthly energy bills, and the calibration of conceptual rainfall-runoff models [10][11][12][13][14][15] where relatively coarse models are used to predict the runoff, given available, often geographically sparse, data of rainfall and potential evapotranspiration. Additional more uncommon applications include the calibration of CMOS device simulations [16] and the calibration of forest growth models [17]. The most popular algorithms in this field are Evolutionary Algorithms like the Genetic Algorithm (GA) [18] and its variants, Shuffled Complex Evolution (SCE-UA) [19], which was specifically developed to calibrate conceptual rainfall-runoff models, Particle Swarm Optimization (PSO) [20], Differential Evolution (DE) [21], and Simulated Annealing (SA) [22], as well as Bayesian Optimization (BO) [23,24].…”