caa a22 4500 467931216 CHVBK 20180406152944.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s10706-005-1136-8 doi (NATIONALLICENCE)springer-10.1007/s10706-005-1136-8 Kabiesz Józef Central Mining Institute, pl. Gwarkow 1, 40-166, Katowice, Poland aut Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks [Elektronische Daten] [Józef Kabiesz] During hard coal mining operations conducted under conditions of rockburst hazard, one of the most important preventive measures can be the prediction of occurrence time and location of the strong seismic mine tremors of energy E s ⩾ 104 J. This is a very difficult task and the way it is being currently performed appears to be unsatisfactory. Therefore, attempts have been made to use neural networks, specifically trained for this application. The paper presents an approach for determining an influence of the type and shape of the input data on the efficiency of such a prediction. The considerations are based on a selected example of the seismic activity recorded during longwall mining operations conducted in one of the Polish mines. Springer, 2006 longwall mining operations nationallicence neural networks nationallicence prediction of mining induced tremors nationallicence Geotechnical & Geological Engineering Kluwer Academic Publishers 24/5(2006-10-01), 1131-1147 0960-3182 24:5<1131 2006 24 10706 https://doi.org/10.1007/s10706-005-1136-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10706-005-1136-8 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Kabiesz Józef Central Mining Institute, pl. Gwarkow 1, 40-166, Katowice, Poland aut NATIONALLICENCE 773 0- Geotechnical & Geological Engineering Kluwer Academic Publishers 24/5(2006-10-01), 1131-1147 0960-3182 24:5<1131 2006 24 10706 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer