Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks.
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.
Teles
Luis Oliva
LO
Departamento de Zoologia e Antropologia, Faculdade de Ciências da Universidade do Porto, Praça Gomes Teixeira, 4099-002, Porto, Portugal. loteles@fc.up.pt
Vasconcelos
Vitor
V
Pereira
Elisa
E
Saker
Martin
M
D000458:Cyanobacteria / Q000254:growth & development*; D016571:Neural Networks, Computer; D011174:Portugal; D012044:Regression Analysis; D045483:Rivers; D012680:Sensitivity and Specificity