Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain)

Abstract

On the Atlantic coasts of Andalucía, chronic spring–summer (March–June) diarrhetic shellfish poisoning (DSP) outbreaks are associated with blooms of Dinophysis acuminata, Claparède and Lachmann. Artificial neural networks (ANNs) have been successfully used to model primary production and have recently been tested for the prediction of harmful algae blooms. In this study, we evaluated the performance of feed forward ANN models trained to predict D. acuminata blooms. ANN models were trained and tested using weekly data (5 previous weeks) of D. acuminata cell counts from eight stations of the Andalucía HAB monitoring programme in the coasts of Huelva between 1998 and 2004. Principal component analysis (PCA) were previously carried out to find out possible similarities within time series from each zone with the aim of reducing the number of areas to model. Our results show that ANN models with a low number of input variables are able to reproduce trends in D. acuminata population dynamics.