SANG X Y, LIN Y, LIU B M,et al.Neural network model of water filling source identification based on water level，water temperature，water intrusion，and water quality[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(5):36-42.

doi:10.16186/j.cnki.1673-9787.2022070002

Received:2022/07/01

Revised:2022/11/28

Published:2024/07/31

Neural network model of water filling source identification based on water level，water temperature，water intrusion，and water quality

SANG Xiangyang1, LIN Yun2, LIU Baomin2, PAN Guoying2

1.Geological Survey Department，China Pingmei Shenma Group，Pingdingshan  467000，Henan，China;2.School of Resources and Environment，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract:The water chemical characteristics of thin limestone karst water in the Taiyuan Formation and thick Ordovician or Cambrian thick limestone karst water in the main coal seam floor of Carboniferous-Permian karst coal field in North China are naturally similar.This similarity poses a risk of misjudgment or even miscalculation when relying solely on certain hydrochemical indexes.  Objectives The water quality indexes of limestone water，L2 limestone water，and some L7 limestone water are similar，making accurate identification challenging.To address this issue，  Methods a neural network model for identifying water sources based on water level，temperature，quantity，and quality was proposed.Taking the filling water source identification of the Pingdingshan mining area as an example，a 15-10-6 neural network model was constructed with 15 indexes as identification factors，including the anion and cation percentages in milligram equivalents，the ratio of sodium to calcium，the ratio of alkali to hardness，ρ（CO32-）,ρ（SO42-）,TDS,ρ（Na+K），water level，dynamic change，water temperature，water intrusion，and attenuation days. Results The experimental results showed that the mean value of all training samples’ fitting to their own water sources exceeded 0.98，which significantly improved the recognition accuracy compared with the modeling method that simply took water quality index as the recognition factor，and could completely and effectively eliminate the misjudgment caused by similar water quality indexes but different water sources.  Conclusions The proposed modeling method had been incorporated into the computer software and mobile app software for identifying water sources in the Pingdingshan mining area.After testing，the recognition accuracy reached 91.3%.

Key words:coal mine water source identification;water level;water temperature;water intrusion;water quality;neural network