This paper presents the results of modelling to predict the effluent biological oxygen demand (BOD5) concentration for primary clarifiers using a hybridisation of unsupervised and supervised artificial neural networks. The hybrid model is based on the unsupervised self-organising map (SOM) whose features were then used to train a multi-layered perceptron, feedforward back propagation artificial neural networks (MLP-ANN). In parallel with this, another MLP-ANN was trained but using the raw data. A comparison of the outputs from the two MLP-ANNs showed that the hybrid approach was far superior to the raw data approach. The study clearly demonstrates the usefulness of the clustering power of the SOM in helping to reduce noise in observed data to achieve better modelling and prediction of environmental systems behaviour.
|Number of pages||8|
|Journal||International Journal of Computer Science and Artificial Intelligence|
|Publication status||Published - Dec 2012|
- Wastewater Treatment Plant; Primary Clarifier Modelling; Neural Networks; Kohonen Self-Organising Map