Two Geoscience Applications by Optimal Neural Network Architecture

Nowadays, artificial neural networks have been successfully applied on several research and application fields. An appropriate configuration for a neural network is a complex task, and it often requires the knowledge of an expert on the application. A technique for automatic configuration for a neural network is formulated as an optimization problem. Two strategies are considered: a mono-objective minimization problem, using multi-particle collision algorithm (MPCA); and a multi-objective minimization problem addressed by the non-dominated sorting genetic algorithm (NSGA-II). The proposed optimization approaches were tested for two application in geosciences: data assimilation for wave evolution equation, and the mesoscale seasonal climate prediction for precipitation. Better results with automatic configuration were obtained for data assimilation than those obtained by network defined by an expert. For climate seasonal precipitation, automatic configuration presented better predictions were presented than ones carried out by an expert. For the worked examples, the NSGA-II presented a superior result for the worked experiments.