光谱学与光谱分析, 2016, 36 (7): 2234, 网络出版: 2016-12-23
KNN结合PCA在激光诱导荧光光谱识别矿井突水中的应用
Application of the Identification of Mine Water Inrush with LIF Spectrometry and KNN Algorithm Combined with PCA
KNN算法 激光诱导荧光 矿井突水 水源识别 KNN algorithm PCA PCA Laser induced fluorescence Mine water inrush Water source identification
摘要
矿井突水的迅速识别与分类对于井下水灾防治工作有着重要的意义。 提出一种KNN结合PCA运用在激光诱导荧光光谱快速识别矿井突水水源中的新方法。 利用激光器发射激光通过可浸入式探头射入水样, 得到四种突水水样共80组荧光光谱数据, 再分别对每组数据进行预处理, 处理后的数据中每种水样取15组数据作为训练集, 共60组, 其余20组作为预测集。 利用主成分分析(PCA)对数据进行处理, 之后在主成分分析的基础上利用KNN算法进行分类识别。 实验过程中, 各预处理方法在主成分个数为2的情况下, 进行KNN算法分类的正确率都达到100%。
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.
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(7): 2234. 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[J]. Spectroscopy and Spectral Analysis, 2016, 36(7): 2234.