Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks

ABSTRACT

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.

KEYWORDS:

• Water quality index
• feedforward artificial neural network
• forward selection
• backward elimination
• artificial neural network
• multiple neural networks

Acknowledgement

The authors would like to acknowledge the support received from the Universiti Sains Malaysia (USM) and Newcastle University, United Kingdom and special gratitude to Department of Environmental (DOE) Malaysia for providing the water quality data for this study.

Disclosure statement

No potential conflict of interest was reported by the authors.