Soft Sensing Modeling Based on Support Vector Regression and Self-Organizing Maps Model Selection for Water Quality Monitoring

This paper studies the application of soft-sensing modeling approach for monitoring surface water quality with artificial intelligence techniques such as SOM (Self-Organizing Maps of Kohonen) and SVM (Support Vector Machines). In the water treatment process, many monitoring parameters are expensive or difficult to measure in real-time, limiting the possibilities for highly efficient control of the water production process. In this work, an intelligent soft-sensor was developed to predict optimal coagulant dosage. It confirmed that the coagulation-flocculation unit is essential in producing drinking water. The soft sensor proposed in this paper contains SOM in feature selection and the SVR method for predicting the optimal coagulant dose values. The surface water quality from Mostaganem's Cheliff Dam was advanced assessed (Algeria). The water quality assessment successfully demonstrated the proposed approach's performance and efficacy, and it can achieve complete expertise in the study area.