Identification of mine water source by random forest combined with laser-induced fluorescence spectra

Abstract

Mine water inrush disaster can quickly cause significant economic losses and casualties because of its strong concealing and rapid burst speed. Quickly identifying the source of mine water inrush is of great practical significance. Compared with the traditional hydrochemical analysis method, the laser-induced fluorescence (LIF) technology has fast reaction speed, high sensitivity, and strong stability, which makes up for the shortcomings of the traditional method. As an integrated algorithm, random forest (RF) has the advantage of high accuracy. A combination of LIF technology and RF algorithm is proposed to identify mine water inrush source rapidly. The experimental samples were collected from a coal mine in Hainan City, Anhui Province, and a total of 525 sets of water samples to be tested for experiments by mixing goaf water and sandstone water into A-G7 species according to different proportions. Moving average smoothing (MA), Savitzky-Golay Smoothing (SG), First derivative (FD), and Second derivative (SD) methods are used to preprocess the original spectral data to reduce the noise and interference information existing in the original spectral data. By comparison, the Moving average smoothing (MA) method has high classification accuracy and is the final method for noise reduction. Then, the RF algorithm is used to delete the less critical spectrum after noise reduction and select the characteristic wavelength with the minimum classification error of 0. Finally, SVM, PCA-SVM, MA-SVM, MA-PCA-SVM, and MA-RF algorithm recognition models were established, respectively. Comparing the prediction accuracy of the test set, the accuracy of the MA-RF algorithm in the five groups of models reached 100%, which can quickly and accurately predict mine water inrush.