Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.
Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.
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DOI：
10.1039/c1em10127g
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发表时间：
2011-06
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影响因子：
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通讯作者：
R. Torres;E. Pereira;V. Vasconcelos;L. O. Teles
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文献类型：
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作者：
R. Torres;E. Pereira;V. Vasconcelos;L. O. Teles
一般回归神经网络（GRNN）的能力，预测的蓝藻在托罗水库（Tâmega河，葡萄牙）的密度，在15天内，根据三年收集的物理和化学数据，进行了评估。开发了几种模型，并根据验证系列的相关值选择了176种。使用的时间滞后为11天，相当于一个样本（夏季为15天，冬季为30天）。使用了该系列的几种组合。应用了从储层三个深度（表面、透光层界限和底部）收集的输入和输出数据。呈现较高平均相关值的模型呈现训练、验证和测试系列的相关性0.991; 0.843; 0.978。该模型有三个时间上独立的系列：首先是测试系列，然后是验证系列，最后是训练系列。只有六个输入变量被认为是显着的性能，这个模型：氨，磷酸盐，溶解氧，水温，pH值和水蒸发，物理和化学参数指的是三个深度的水库。这些变量是共同的下四个最好的模型产生，虽然这些包括其他输入变量，其性能并不比选定的最佳模型。
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.