Application of an unsupervised artificial neural network technique to multivariant surface water quality data

• Published in: Ecological research Vol. 24; no. 1; pp. 163 - 173
• Main Authors: Çinar, Özer, Merdun, Hasan
• Format: Journal Article
• Language: English
• Published: Japan Japan : Springer Japan 2009; Springer Japan; Blackwell Publishing Ltd
• Subjects: Agricultural pollution; Agricultural runoff; Artificial intelligence; Behavioral Sciences; Biomedical and Life Sciences; Clustering; Dissolved oxygen; Ecology; Evolutionary Biology; Forestry; Groundwater pollution; k-Means clustering algorithm; Kohonen self-organising feature maps; Life Sciences; Neural networks; Organic matter; Original Article; Plant Sciences; Pollution sources; Runoff; Surface water; Surface water quality; Urban agriculture; Urban runoff; Variable dependencies; Wastewater discharges; Water pollution; Water quality; Zoology; Means clustering algorithm; Kohonen self-organising feature maps; Variable dependencies; Surface water quality; Clustering
• DOI: 10.1007/s11284-008-0495-z
• EISSN: 1440-1703
• ISSN: 0912-3814

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the Kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies-Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, K, Cl, NH₄-N, NO₂-N, o-PO₄, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.