Abstract
Real-time monitoring of surface water quality is an intractable problem. A Soft-sensor method based on fuzzy neural network (FNN) is proposed to solve this problem in this paper. Firstly, the river data was analyzed by principal component analysis (PCA) to obtain related variables such as dissolved oxygen (DO) and ammonia nitrogen (NH3-N). Secondly, a multi-input soft-sensor method based on FNN is designed. The training data is preprocessed by Hierarchical Clustering and K-means algorithm (H-K algorithm), which improves the accuracy of the soft-sensor method. Finally, the soft-sensor method is packaged and applied to Beijing Tonghui River. The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.
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