Soft-sensor Method for Surface Water Qualities Based on Fuzzy Neural Network

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.

1 Introduction

With the rapid growth of the social economy and people’s living standards, people’s demand for water resources is increasing, causing continuous high-intensity damage to the water environment system [1]–[2]. Surface water pollution in most areas of China is serious. The current state of water quality is worrying. The discharge of production wastewater, domestic wastewater, and the flow of surface runoffs into rivers caused by rainfall runoff have led to the deterioration of surface water quality [3]. Therefore, it is particularly important to control, manage and protect the pollution of surface water.