Innovation Center of the Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11120, Belgrade, Serbia,
Environ Sci Pollut Res Int. 2013 Dec;20(12):9006-13. doi: 10.1007/s11356-013-1876-6. Epub 2013 Jun 14.
The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ± 10 %. In case of the MLR, only 55 % of predictions were within the error of less than ± 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.
本研究的目的是利用非特定水质参数创建人工神经网络 (ANN) 模型，并检验三种不同的 ANN 架构（广义回归神经网络 (GRNN)、反向传播神经网络 (BPNN) 和递归神经网络 (RNN)）在预测多瑙河溶解氧 (DO) 浓度方面的准确性。该神经网络模型是使用多瑙河贝兹丹监测站收集的实测数据开发的。用于 ANN 模型的输入变量是水流、温度、pH 值和电导率。该模型使用 2004 年至 2008 年的可用数据进行训练和验证，并使用 2009 年的数据进行测试。根据与测试数据的比较，创建的架构的性能顺序为 RNN > GRNN > BPNN。ANN 结果与多元线性回归 (MLR) 模型使用多个统计指标进行了比较。RNN 模型与 MLR 模型的比较表明，RNN 模型的性能要好得多，因为 RNN 模型对测试数据的所有预测都在误差小于±10%的范围内。对于 MLR，只有 55%的预测在误差小于±10%的范围内。开发的 RNN 模型可用于预测河水中的 DO。
