Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks |
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Authors: | Luis Oliva Teles Vitor Vasconcelos Luis Oliva Teles Elisa Pereira Martin Saker Vitor Vasconcelos |
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Institution: | 1. Departamento de Zoologia e Antropologia, Faculdade de Ciências da Universidade do Porto, Pra?a Gomes Teixeira, 4099-002, Porto, Portugal 2. CIIMAR, Centro Interdisciplinar de Investiga??o Marinha e Ambiental, Rua dos Bragas 289, 4050-123, Porto, Portugal
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Abstract: | 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. |
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Keywords: | Cyanobacteria Neural network Forecasting Modelling Water quality management Artificial reservoir Eutrophication |
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