Forecasting of cyanobacterial density in Torrao reservoir using artificial neural networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Rita Torres (Autor) Outra; Elisa Pereira (Autor) Outra; Vitor Vasconcelos (Autor) FCUP; Luis Oliva Teles (Autor) FCUP

Revista

Título: Journal of Environmental Monitoring

Vol. 13

Páginas: 1761-1767

ISSN: 1464-0325

Editora: Royal Society of Chemistry

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 8 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Química

Outras Informações

ID Authenticus: P-002-QTW

DOI: 10.1039/c1em10127g

Abstract (EN): The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrao reservoir (Tamega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7