Abstract
This paper addresses the use of neural networks as a metamodelling technique for discrete event stochastic simulation to reduce significantly the computational burden involved by the simulations. A sophisticated computer model has been developed to anticipate the propagation of the green alga Caulerpa taxifolia in the northwestern Mediterranean sea. The simulation model provides reliable predictions, a couple of years in advance, of the covered surfaces. To reduce the heavy computational burden involved by the simulation, a neural network was successfully trained on artificially generated data provided by the simulation runs to provide accurate forecasts 12 years in advance, along with associated confidence intervals. The neural-network metamodel is competitive in accuracy when compared to the simulation itself and, once trained, can operate in nearly real time.
Published Version
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