Research on spectral water quality monitoring, analysis and recognition system based on PCA-LSSVM

Coal mine water disasters are one of the main disasters threatening the safe production of mines, and the accurate identification of water - filling sources has become a crucial step in preventing and controlling water disasters. In response to the problems of low efficiency and poor real - time performance in traditional water quality analysis methods, this study proposes and implements a water source identification system based on intelligent algorithms. The system integrates principal component analysis (PCA) and least squares support vector machine (LSSVM) to construct a PCA - LSSVM hybrid model. First, the model uses PCA to perform dimensionality reduction on the spectral monitoring data of key ions, eliminating redundant information. Then, it combines LSSVM for non - linear classification training, significantly improving the efficiency and accuracy of water source feature extraction. The Schukarev classification method is introduced to systematically classify water - filling sources based on the dominant ion combinations in groundwater, providing a scientific basis for tracing the water inrush channels. The developed high - precision spectral water quality sensor and its supporting monitoring system, compared with traditional ion - electrode sensors, have an approximately 30% increase in detection accuracy, a response time reduced to the second level, and a significantly enhanced anti - interference ability. The system has been deployed on a large scale in multiple mining areas. The measured data shows that the accuracy of water source type identification is stably above 90%, and the false alarm rate is lower than 5%, providing reliable technical support for water disaster early warning.