Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

Luis Oliva Teles (Autor) Outra; Vitor Vasconcelos (Autor) Outra; Luis Oliva Teles (Autor) FCUP; Elisa Pereira (Autor) Outra; Martin Saker (Autor) Outra; Vitor Vasconcelos (Autor) Outra

Revista

Título: Environmental Management

Vol. 38

Páginas: 227-237

ISSN: 0364-152X

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 29 Citações

Scopus - 34 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da terra e ciências do ambiente

Outras Informações

ID Authenticus: P-004-J1S

DOI: 10.1007/s00267-005-0074-9

Abstract (EN): In this work, time series neural networks were used to predict the occurrence of toxic cyanobacterial blooms in Crestuma Reservoir, which is an important potable water supply for the Porto region, located in the north of Portugal. These models can potentially be used to provide water treatment plant operators with an early warning for developing cyanobacteria blooms. Physical, chemical, and biological parameters were collected at Crestuma Reservoir from 1999 to 2002. The data set was then divided into three independent time series, each with a fortnightly periodicity. One training series was used to "teach" the neural networks to predict results. Another series was used to verify the results, and to avoid over-fitting of the data. An additional independently collected data series was then used to test the efficacy of the model for predicting the abundance of cyanobacteria. All of the models tested in this study incorporated a prediction time (look-ahead parameter) equal to the sampling interval (two weeks). Various lag periods, from 2 to 52 weeks, were also investigated. The best model produced in this study provided the following correlations between the target and forecast values in the training, verification, and validation series: 1.000 (P = 0.000), 0.802 (P = 0.000), and 0.773 (P = 0.001), respectively. By applying this model to the three-year data set, we were able to predict fluctuations in cyanobacteria abundance in the Crestuma Reservoir, with a high level of precision. By incorporating a lag-period of eight weeks, we were able to detect secondary fluctuations in cyanobacterial abundance over the annual cycle.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11