Title :
Study on multivariate forecast model of PingdingshanNO.10 mine gas content based on BP neural network
Author :
Tianxuan Hao ; Ling Shi
Author_Institution :
Sch. of Safety Sci. & Eng., Henan Polytech. Univ., Jiaozuo, China
Abstract :
The mathematic principles and numerical algorithm of BP neural network for gas contents were firstly studied, Then, the actual measurement data of gas contents during geological prospecting and mining of PingdingshanNO.10WU9-10 mine were collected, and 12 reliable dots were gained. By selecting 3 factors including depth, coal seam thickness and coal roof lithology as the input element, and the multivariate forecast models of gas contents based on BP neural network were respectively constructed. According to the calculation and evaluation of results, accuracy of the model to meet the requirements of engineering precision, indicated that BP neural network to predict mine e Pingdingshan 9-10 ten gas content of coal seam gas is feasible.
Keywords :
backpropagation; forecasting theory; mining industry; neural nets; BP neural network; PingdingshanNO.10 mine gas content; PingdingshanNO.10WU9-10 mine; actual measurement data; coal roof lithology; coal seam thickness; depth; engineering precision; geological prospecting; mathematic principles; mining; multivariate forecast models; numerical algorithm; Coal; Data models; Drilling machines; Fuel processing industries; Mathematical model; Neural networks; Predictive models; Gas geology; Information; Management; Mapguide;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223139
