Abstract：Rapid identification and classification of mine water inrush is important for flood prevention work underground. This paper proposed a method of KNN combined with PCA identification of water inrush in mine with the laser induced fluorescence spectrum with an immersion probe laser into water samples. The fluorescence spectra of 4 kinds of water samples were obtained. For each set of data preprocessing, the processed data in each sample from 15 sets of data as the training setwith a total of 60 groups. The other 20 groups were used as the prediction set. The data were processed by principal component analysis (PCA), and then the KNN algorithm was used to classify and identify the principal component analysis. During the experiment, the pretreatment method in the principal component number is 2 while the correct rate has reached 100% by KNN classification algorithm.
Key words：KNN algorithm;PCA;Laser induced fluorescence;Mine water inrush;Water source identification
何晨阳，周孟然*，闫鹏程. KNN结合PCA在激光诱导荧光光谱识别矿井突水中的应用[J]. 光谱学与光谱分析, 2016, 36(07): 2234-2237.
HE Chen-yang, ZHOU Meng-ran*, YAN Peng-cheng. Application of the Identification of Mine Water Inrush with LIF Spectrometry and KNN Algorithm Combined with PCA. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(07): 2234-2237.
