AdaBoost算法在矿井突水水源的荧光光谱识别中的研究  

Research of the AdaBoost Arithmetic in Recognition and Classifying of Mine Water Inrush Sources Fluorescence Spectrum

作　　者：周孟然[1] 李大同 胡锋 来文豪 王亚[1] 朱松 ZHOU Meng-ran;LI Da-tong;HU Feng;LAI Wen-hao;WANG Ya;ZHU Song(College of Electrical and Information Engineering,Anhui University of Scienceand Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《光谱学与光谱分析》2019年第2期485-490,共6页Spectroscopy and Spectral Analysis

基　　金：国家"十二五"科技支撑计划重点项目(2013BAK06B01);国家安全生产重大事故防治关键技术科技项目(anhui-0001-2016AQ);国家自然科学基金项目(51174258)资助

摘　　要：矿井突水是影响矿井安全生产的重要因素之一,如果矿井发生突水,能够快速、准确地判别突水水源类型是治理矿井突水灾害保证生产安全的重要环节,因此,建立一个能够快速识别矿井突水水源的模型具有重要的意义。水化学分析法作为在传统的矿井突水水源类型识别方法里应用最为广泛的识别方法,通过获得相应的pH值、离子浓度、电导率等参数,然后利用这些参数来建立突水水源的类型识别模型对矿井突水的类型进行判别。针对这种传统矿井突水水源识别方法在判别时间上耗时长和识别准确率低等不足,鉴于LIF技术具有分析速度快、灵敏度高等优点,提出了将线性判别分析(LDA)算法作为弱分类器的自适应提升(AdaBoost)算法用于激光诱导荧光(LIF)光谱识别矿井突水水源的新方法。用于实验的九种水样(每种水样各取50个样本)由淮南地区某矿的老空水、灰岩水以及按不同比例混合的老空水与灰岩水的七种混合水构成。将405nm激光器发射的激光打入被测水体并采集荧光光谱数据,然后对采集到450组荧光光谱数据进行分析,取其中360组光谱数据(每种水样各40组)用作训练集,取剩余90组光谱数据用作测试集。分别选取三种算法针对水样的激光诱导荧光光谱的分类进行了建模并将三种结果进行对比。首先利用决策树算法对光谱进行分类识别,在节点个数为8时决策树对测试集的分类效果最好,分类准确率达到91.11%。然后针对决策树算法分类效果的不足,利用决策树算法作为弱分类器的AdaBoost算法,当选取节点个数为9的决策树作为弱分类器的时,对训练集的分类准确率为97.78%。最后针对基于决策树的AdaBoost算法的泛化性能不足和为了获得更好的分类效果,提出了基于LDA算法作为弱分类器的AdaBoost算法,在设置迭代次数为150后对水样光谱数据分类准确率可以达到100%。通�The water inrush is one of the most important elements that can influence the mining safety,and being able to recognize the category of water inrush sources accurately and rapidly will greatly enhance the mining safety condition when water inrush happens accidentally.Therefore,it is extremely important and necessary to create a model system that can recognize water inrush sources effectively.The water chemistry analytical method is the widest used method to recognize water inrush sources among traditional methods;in this method,we build a model system by using ph,ionic concentration,conductivity and so on,then use that model system to recognize water inrush sources.However,the water chemistry analytical method has disadvantages that usually be time-costing and of low accuracy.This essay will deal with this problem and introduce the AdaBoost method that uses LDA as weak classifier based on LIF technology because of the rapidnessand high sensitivity of LIF technology.In this research,there are nine kinds of waters from a certain mine in the Huainan City considered and fifty independent samples in each kind of water,limestone water,high pressure water from floor of coal seam and gob areas,and seven different proportion mixture of those two kind of water.Emit laser from the 405 nm laser emitter into laboratory water samples and collect experiment statistics of fluorescence spectrum,analyze these 450 water samples by select 360samples(40samples of each kind of water source)as a training set first and set other 90 samples as a training set.In this essay,we use three different kinds of arithmetic to build three different model systems and compare results from each model system.First of all,we use decision-making tree to recognize and classify different fluorescence spectrum,we get the best outcome and the accuracy rate is 91.11%at that time when the node number is 8.Then,we use the AdaBoost arithmetic and set the decision-making tree as the weak classifier according to the shortage of the decision-making tree,and we get

关 键 词：矿井突水 LIF技术 决策树 LDA ADABOOST 

分 类 号：O657.3[理学—分析化学]