Forecasting of cyanobacterial density in Torrao reservoir using artificial neural networks

• Type: Article in International Scientific Journal
• Date: 2011

Authors

Rita Torres (Author) Other; Elisa Pereira (Author) Other; Vitor Vasconcelos (Author) FCUP; Luis Oliva Teles (Author) FCUP

Journal

Title: Journal of Environmental Monitoring

Vol. 13

Pages: 1761-1767

ISSN: 1464-0325

Publisher: Royal Society of Chemistry

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 8 Citations

Scientific classification

FOS: Natural sciences > Chemical sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7