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https://doi.org/10.1007/s12665-017-7212-6

has been cited by the following article:

• TITLE: Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

  AUTHORS: Mingcheng Ning, Haifeng Lu

  KEYWORDS: Mine Water Inrush, Analysis of Hydrochemical Characteristics, Principal Component Analysis (PCA), Back Propagation Neural Networks, Water Source Identification

  JOURNAL NAME: International Journal of Geosciences, Vol.14 No.8, August 17, 2023

  ABSTRACT: It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.