AdaBoost算法在矿井突水水源的荧光光谱识别中的研究

doi:10.3964/j.issn.1000-0593(2019)02-0485-06

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Abstract 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 405nm laser emitter into laboratory water samples and collect experiment statistics of fluorescence spectrum, analyze these 450 water samples by select 360 samples (40 samples 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 the best accuracy rate of classifying training sets of 97.78% when selecting a decision-making tree whose node number is 9 as the weak classifier. And last, we introduce a AdaBoost arithmetic base on setting LDA arithmetic as the weak classifier to get better classifying results according to the generalization shortage of AdaBoost arithmetic which bases on decision-making tree, and finally we get the spectrum accuracy rate of 100% when iterate 150 times. As we can get from our experiment, classifying arithmetic that integrates the learning arithmetic is much better than other traditional classifying arithmetic, for instance, compared with the decision-making tree arithmetic, AdaBoost arithmetic which sets decision-making tree as its weak classifier can enhance the accuracy rate of classifying testing set from 88.89% to 97.78% and enhance the accuracy rate of classifying training set from 99.72% to 100% when the node number is 9; then compared with the AdaBoost arithmetic which sets decision-making tree as its weak classifier, the AdaBoost arithmetic which uses LDA as its weak classifier can enhance the accuracy rate of classifying sample water fluorescence spectrum testing set from 97.78% to 100% and enhance the accuracy rate of classifying sample water fluorescence spectrum training set to 100% as well, and we can get better recognition outcomes and make our model system have better generalization by using such strategy at the same time. Therefore, it is extremely fair to say that using AdaBoost-LDA arithmetic to classify fluorescence spectrum to recognize and alarm water inrush sources is effective and feasible.

Key words：Mine water inrush; LIF technology; Decision-making tree; LDA; AdaBoost

Received: 2018-01-28 Accepted: 2018-06-12

ZTFLH:

O657.3

Corresponding Authors: LI Da-tong E-mail: ldt5737@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)02-0485-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I02/485

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