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

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

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.

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.