Determination and prediction on “three zones” of coal spontaneous combustion in a gob of fully mechanized caving face

[1]  C. Ellyett,et al.  Thermal infrared imagery of The Burning Mountain coal fire , 1974 .

[2]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[3]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[4]  Robert B. Finkelman,et al.  Potential health impacts of burning coal beds and waste banks , 2004 .

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Glenn B. Stracher,et al.  Coal fires burning around the world: a Global Catastrophe , 2004 .

[7]  Edward L. Heffern,et al.  Geologic history of natural coal-bed fires, Powder River basin, USA , 2004 .

[8]  R. S. Chatterjee,et al.  Coal fire mapping from satellite thermal IR data ¿ A case example in Jharia Coalfield, Jharkhand, India , 2006 .

[9]  Wolfgang Wagner,et al.  Uncontrolled coal fires and their environmental impacts : investigating two arid mining regions in North - Central China , 2007 .

[10]  Eliseo Monfort,et al.  Environmental characterization of burnt coal gangue banks at Yangquan, Shanxi Province, China , 2008 .

[11]  B. V. Rao,et al.  Hardgrove grindability index prediction using support vector regression , 2009 .

[12]  Qin Xu,et al.  Analysis and key control technologies to prevent spontaneous coal combustion occurring at a fully mechanized caving face with large obliquity in deep mines , 2009 .

[13]  Qiong Li,et al.  On-line monitoring the performance of coal-fired power unit: A method based on support vector machine , 2009 .

[14]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[15]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[16]  David J. Williams,et al.  Greenhouse gas emissions from low-temperature oxidation and spontaneous combustion at open-cut coal mines in Australia , 2009 .

[17]  Xianliang Meng,et al.  Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network , 2009 .

[18]  Jianjun Wu,et al.  Risk assessment of underground coal fire development at regional scale , 2011 .

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Wang Jun,et al.  Assessment of the contribution of in-situ combustion of coal to greenhouse gas emission; based on a comparison of Chinese mining information to previous remote sensing estimates , 2011 .

[21]  C. Özgen Karacan,et al.  Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction , 2011 .

[22]  Claudia Kuenzer,et al.  Geomorphology of coal seam fires , 2012 .

[23]  Allan Kolker,et al.  Gas emissions, minerals, and tars associated with three coal fires, Powder River Basin, USA. , 2012, The Science of the total environment.

[24]  Siba Sankar Mahapatra,et al.  Fuzzy c-means clustering approach for classification of Indian coal seams with respect to their spontaneous combustion susceptibility , 2012 .

[25]  Yuanping Cheng,et al.  New technological partition for “three zones” spontaneous coal combustion in goaf , 2013 .

[26]  Abdullah Gani,et al.  Wind turbine power coefficient estimation by soft computing methodologies: Comparative study , 2014 .

[27]  Weixing Huang,et al.  Comparative study of explosion processes controlled by homogeneous and heterogeneous combustion mechanisms , 2014 .

[28]  Yan Zhou,et al.  Forecasting of coal seam gas content by using support vector regression based on particle swarm optimization , 2014 .

[29]  Sungzoon Cho,et al.  Approximating support vector machine with artificial neural network for fast prediction , 2014, Expert Syst. Appl..

[30]  Alireza Bahadori,et al.  Prediction of natural gas flow through chokes using support vector machine algorithm , 2014 .

[31]  Tongqiang Xia,et al.  A fully coupled hydro-thermo-mechanical model for the spontaneous combustion of underground coal seams , 2014 .

[32]  Mat Kiah M.L.,et al.  Wind turbine power coefficient estimation by soft computing methodologies: Comparative study , 2014 .

[33]  Claudia Kuenzer,et al.  Coal fires in China over the last decade: A comprehensive review , 2014 .

[34]  Shengqiang Yang,et al.  Coal spontaneous combustion prediction in gob using chaos analysis on gas indicators from upper tunnel , 2015 .

[35]  C. Zhang,et al.  Estimation of higher heating value of coal based on proximate analysis using support vector regression , 2015 .

[36]  Wei Gao,et al.  Flame behaviors and pressure characteristics of vented dust explosions at elevated static activation overpressures , 2015 .

[37]  Q. Feng,et al.  The use of an artificial neural network to estimate natural gas/water interfacial tension , 2015 .

[38]  Jiyuan Zhang,et al.  Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network , 2015 .

[39]  Yang Chen,et al.  Development of a spontaneous combustion TARPs system based on BP neural network , 2015 .

[40]  Yusuf Al-Turki,et al.  Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study , 2015 .

[41]  Jun Deng,et al.  Experimental studies of spontaneous combustion and anaerobic cooling of coal , 2015 .

[42]  Yong-liang Xu,et al.  Study on the characteristics of gas explosion affected by induction charged water mist in confined space , 2016 .

[43]  Sun Shaohua,et al.  Risk analysis of coal self-ignition in longwall gob: A modeling study on three-dimensional hazard zones , 2016 .

[44]  Guomin Zhang,et al.  A risk assessment method to quantitatively investigate the methane explosion in underground coal mine , 2017 .