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 signi"cantly 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 arti"cially generated data provided by the simulation runs to provide accurate forecasts 12 years in advance, along with associated con"dence intervals. The neuralnetwork metamodel is competitive in accuracy when compared to the simulation itself and, once trained, can operate in nearly real time. ( 2000 Elsevier Science B.V. All rights reserved.