Application of artificial neural networks for water quality prediction

• Published in: Neural computing & applications 2013-05, Vol.22 (Suppl 1), p.187-201
• Main Authors: Najah, A., El-Shafie, A., Karim, O. A., El-Shafie, Amr H.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Freshwater; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-0940-3

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.