文章基本信息

标题：ARTIFICIAL NEURAL NETWORK PREDICTION MODEL OF KARST WATER IN COAL MINES
本地全文：下载
作者：Pinghua Huang ; Xinyi Wang ; Sumin Han 等
期刊名称：Fresenius Environmental Bulletin
印刷版ISSN：1018-4619
出版年度：2019
卷号：28
期号：1
页码：452-458
语种：English
出版社：PSP Publishing
摘要：A neural network algorithm based on backpropagation 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.
关键词：Coal mine;Karst water;neural network algorithm;ANN prediction model;key influencing factors;sensitivities analysis