期刊
INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT
卷 15, 期 1, 页码 79-87出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/15715124.2016.1256297
关键词
Water quality index; feedforward artificial neural network; forward selection; backward elimination; artificial neural network; multiple neural networks
资金
- Universiti Sains Malaysia (USM)
- Newcastle University, United Kingdom
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据