Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.

The objectives of this research were to determine spatial patterns of fish well and this spatial patterns were closely associated with physical habitat and chemical water quality. Such relations of physical habitat and chemical water quality to high trophic-level biota (i.e., fish) are very complex in analyzing the cause-effective relations due to large ecological dataset, thus an approach of self-organizing map (SOM) has been proposed in elucidating complex ecological relations of aquatic ecosystems. Communities from up-stream to down-stream of Geum River using an approach of Self-Organizing Maps (SOMs) and analyze their relations to water chemistry. For the study, fish and water samplings were conducted in 130 different streams and rivers during 2008 - 2009. The analysis of data using the SOMs model showed that fish community had longitudinal gradients of up-stream, mid-stream and down-stream. The clustering of the trained SOMs units reflected the stream morphology, land-use pattern and water quality, resulting in influenced the ecological trophic compositions and tolerance of top-level fish in the aquatic ecosystem