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Water Quality Prediction in a Reservoir: Linguistic Model Approach for Interval Prediction

Jin-Il Park, Nahm-Chung Jung, Keun-Chang Kwak, and Myung-Geun Chun*
International Journal of Control, Automation, and Systems, vol. 8, no. 4, pp.868-874, 2010

Abstract : It is difficult to predict water quality in a reservoir because of the complex physical, chemical, and biological processes involved. In contrast to the well-known numeric models and artificial neural network models, Linguistic Models (LM) with context-based fuzzy clustering can offer reliable predictions of water quality. The main characteristics of LM are that it is user-centric and that it inher-ently dwells upon collections of highly interpretable and user-oriented entities, such as information granules. In this paper, we propose a model for evaluating water quality and then evaluate the effec-tiveness of the proposed method by performing comparisons on water quality data sets from a reservoir. Finally, we found that the proposed method not only has the better prediction performance than other models, but also can offer reliable intervals for uncertainty evaluation about the water quality.

Keyword : Context-based fuzzy clustering, interval prediction, linguistic model, water quality prediction.

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