Wedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean sea

Abstract

We discuss the use of supervised neural networks as a metamodelling technique for discrete event stochastic simulation in order to reduce significantly the computational burden involved by discrete simulations. A sophisticated computer model, coupling a Geographical Information System with a stochastic discrete event simulator, has been developed to anticipate the propagation of the green alga {\em Caulerpa taxifolia} in the North-Western Mediterranean sea. The simulation model provides reliable predictions, a couple of years in advance, of: i) the local expansion patterns of the alga, ii) the increase of {\em C. taxifolia} biomass and iii), the covered surfaces. However because the algorithmic model accounts for spatial interactions and anthropic dispersion/activities such as eradication, introduction of specific predators etc., simulations are extremely time and memory consuming. Therefore, to reduce the computational burden, 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 ability of the neural networks to capture the underlying physics of the phenomena is clearly illustrated by several preliminary experiments on a large coastal area. The neural network is able to construct, on this site, estimates of the {\em Caulerpa taxifolia} expansion 12 years in advance in good agreement with the simulation trajectories.