ARTIFICIAL NEURAL NETWORK PREDICTION MODEL OF KARST WATER IN COAL MINES

被引：0
|
作者：
Huang, Pinghua [1 ,2 ]
Wang, Xinyi [1 ,2 ]
Han, Sumin [3 ]
机构：
[1] Henan Polytech Univ, Sch Resources & Environm Engn, Jiaozuo 454000, Peoples R China
[2] Collaborat Innovat Ctr Coalbed Methane & Shale Ga, Jiaozuo, Henan, Peoples R China
[3] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454000, Peoples R China
来源：
FRESENIUS ENVIRONMENTAL BULLETIN | 2019年 / 28卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Coal mine; Karst water; neural network algorithm; ANN prediction model; key influencing factors; sensitivities analysis; RIVER; DESIGN; ENERGY;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
A neural network algorithm based on back-propagation algorithm was proposed in this paper. An artificial neural network prediction model for karst water in coal mines was established for the first time to study the supply characteristics of karst water and its key influencing factors. The default factor method was utilized to determine the sensitivities of four influencing factors. Results showed that the water level prediction results accorded with the actual water level. Precipitation had the greatest influence on groundwater level, followed by pit displacement. Moreover, long-term stable supply was the main influencing factor of groundwater level. The proposed prediction model exhibits strong applicability and broad application prospect. This research provides scientific basis for water-level prediction and water inrush prevention.
引用
收藏
页码：452 / 458
页数：7
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