Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea
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.
metamodelling caulerpa taxifolia neural networks discrete event simulation invasive species