Skip to main content

METHODS article

Front. Environ. Sci.
Sec. Water and Wastewater Management
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1392496

Identification of Mine Water Source by Random Forest Combined with Laser-Induced Fluorescence Spectra

Provisionally accepted
Xiaona Ma Xiaona Ma 1Pengcheng Yan Pengcheng Yan 2*Kun Wang Kun Wang 2*
  • 1 School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan, China
  • 2 School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Laser induced fluorescence spectroscopy, Mine water source, Water source identification, random forest, Preprocess

    Received: 27 Feb 2024; Accepted: 01 Jul 2024.

    Copyright: © 2024 Ma, Yan and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Pengcheng Yan, School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
    Kun Wang, School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.