Torres Rita, Pereira Elisa, Vasconcelos Vítor, Teles Luís Oliva
Departamento de Biologia - Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4069-007 Porto, Portugal.
J Environ Monit. 2011 Jun;13(6):1761-7. doi: 10.1039/c1em10127g. Epub 2011 May 6.
The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega 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.
评估了广义回归神经网络（GRNN）基于三年收集的物理和化学数据预测托朗水库（葡萄牙塔梅加河）15天内蓝藻密度的能力。开发了多个模型，并根据验证序列的相关值选择了176个模型。使用了11的时间滞后，相当于一个样本（夏季为15天，冬季为30天）。使用了该序列的几种组合。应用了从水库三个深度（水面、真光层界限和底部）收集的输入和输出数据。呈现出较高平均相关值的模型在训练、验证和测试序列中的相关系数分别为0.991、0.843、0.978。该模型的三个序列在时间上相互独立：首先是测试序列，然后是验证序列，最后是训练序列。只有六个输入变量被认为对该模型的性能有显著影响：氨、磷酸盐、溶解氧、水温、pH值和水蒸发量，这些是指水库三个深度的物理和化学参数。这些变量在接下来产生的四个最佳模型中是共同的，尽管这些模型还包括其他输入变量，但其性能并不比所选的最佳模型更好。
