RF-CARS结合LIF光谱用于矿井涌水的预测评估

doi:10.3964/j.issn.1000-0593(2020)07-2170-06

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Abstract Quick and accurate identification of mine water inflow has important research significance for preventing coal mine flood accidents, the laser-induced fluorescence (LIF) spectroscopyis used to integrate withthe intelligent classificationalgorithm to identify the mine water inflow, it breaks the shortcomings of traditional water chemistry methods, such as long time consuming, etc., and has the characteristics of high sensitivity and fast response. However, these currently used algorithms can only rely on the classification accuracy to qualitatively discriminate the types of water samples from different mine water inflow. This paper combines the random forest algorithm with the competitive adaptive weighting algorithm (RF-CARS), the partial least squares regression (PLSR) model based on fluorescencespectrum data from the laser-induced fluorescence was used to predict the water inflow in different mines and to achieve quantitative assessment of water samples. Firstly, 300 sets of mine water inflow samples mixed with different sandstone waters based on goaf water were collected, and the collected water samples were randomly divided into the calibration set and the prediction setaccording to the ratio of 4∶1, a total of 240 sets of calibration sets were used to establish a regression model, a total of 60 sets of prediction sets were used to predict different water samples, and a laser-induced fluorescence inflow spectroscopy system was built to complete the acquisition of spectral data and generated a fluorescence spectrum. Then the original fluorescence spectrum was denoised by S-G convolution smoothing method and Lowess smoothing method, and it was found that the processed fluorescence spectrum was more dispersed than the original spectrum, which was suitable for spectral analysis, the prediction accuracy of two denoising methods were compared, the Lowess was chosen as the final denoising method. Then, the RF algorithm was used to reduce the spectral attributes with low attribute importance after denoising, according to the performance of the optimal regression model, the 223 reduced attributes were selected and then it was used for the secondary attribute reduction of the CARS algorithm. The PLSR model was established based on 77 spectral attribute data selected according to the principle of minimum cross validation root mean square error in the sampling process of CARS algorithm. Finally, we compared with the full spectrum, other variable selection methods, and different regression models, the RF-CARS algorithm had the best streamlining effect, and the total spectral modeling attribute was reduced from 2 048 to 77, the model prediction set determination coefficient R²_pre increased from 0.991 4 to 0.996 7, the predicted root mean square error RMSEP decreased from 0.029 4 to 0.018 3, the prediction accuracy was improved, and the remaining evaluation indicators were relatively good. The experimental results show that the RF-CARS combined with laser induced fluorescence technology can quickly and accurately predict mine water inflow, the simplified spectral attributes are used to establish regression model, which provides a theoretical guarantee for real-time quantitative evaluation of mine water inflow.

Key words：Laser-induced fluorescence spectroscopy; Regression model; Random forest; Competitive adaptive reweighted sampling; Mine water inflow

Received: 2019-07-16 Accepted: 2019-11-08

ZTFLH:

O657.3

Corresponding Authors: ZHOU Meng-ran E-mail: mrzhou8521@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)07-2170-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I07/2170