Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks
Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks
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DOI：
10.1080/15715124.2016.1256297
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发表时间：
2017-01-01
影响因子：
2.5
通讯作者：
Zhang, Jie
中科院分区：
文献类型：
--
作者：
Ahmad, Z.;Rahim, N. A.;Zhang, Jie
本文提出了一种实时预测水质指数的方法，该方法将非真实的测量的生物需氧量和化学需氧量从模型输入中剔除。在这项研究中，前馈人工神经网络被用来模拟的WQI在霹雳州流域马来西亚由于其在建模非线性系统的能力。结果表明，所开发的单一前馈神经网络模型可以预测WQI非常好的决定系数R2和均方误差（MSE）的0.9090和0.1740的未知验证数据，分别。除此之外，多个神经网络在预测WQI时的聚合进一步提高了对未知验证数据的预测性能。采用前向选择和后向淘汰选择组合方法对多个神经网络进行联合收割机组合，两种方法组合的神经网络分别为6个和5个，R2和MSE分别为0.9340、0.9270和0.1156、0.1256。它清楚地表明，组合多个神经网络确实提高了WQI预测的性能。
This paper proposes a method for the real-time prediction of water quality index (WQI) by excluding the biological oxygen demand and chemical oxygen demand, which are not measured in real time, from the model inputs. In this study, feedforward artificial neural networks are used to model the WQI in Perak River basin Malaysia due to its capability in modelling nonlinear systems. The results show that the developed single feedforward neural network model can predict WQI very well with the coefficient of determination R2 and mean squared error (MSE) of 0.9090 and 0.1740 on the unseen validation data, respectively. In addition to that, the aggregation of multiple neural networks in predicting the WQI further improves the prediction performance on the unseen validation data. Forward selection and backward elimination selective combination methods are used to combine multiple neural networks and both methods lead to 6 and 5 networks being combined with R2 and MSE of 0.9340, 0.9270 and 0.1156, 0.1256, respectively. It is clearly shown that combining multiple neural networks does improve the performance for WQI prediction.