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Application of the spatial data mining module in analysis of mining ground deformation factors

 
1
Wroclaw University of Technology, Institute of Mining Engineering
2
Wroclaw University of Technology, The Faculty of Geoengineering, Mining and Geology
Mining Science 2013;20:5–17
KEYWORDS
ABSTRACT
Spatial data mining methods for example those based on artificial neural networks (ANN) allow extraction of information from databases and detection of otherwise hidden patterns occurring in these data and in consequence acquiring new knowledge on the analysed phenomena or processes. One of these techniques is the multivariate statistical analysis, which facilitates identification of patterns otherwise difficult to observe. In the paper an attempt of applying self-organising maps (SOM) to explore and analyse spatial data related to studies of ground subsidence associated with underground mining has been described. The study has been carried out on a selected part of a former underground coal mining area in SW Poland with the aim to analyse the influence of particular ground deformation factors on the observed subsidence and the relationships between these factors. The research concerned the uppermost coal panels and the following factors: mining system, time of mining activity and inclination, thickness and depth below the ground of the exploited coal panels. It has been found that the exploratory spatial data analysis can be used to identify relationships in multidimensional data related to mining induced ground subsidence. The proposed approach may be found useful in identification of areas threatened by mining related subsidence and in creating scenarios of developing deformation zones and therefore aid spatial development of mining grounds.
CORRESPONDING AUTHOR
Jan Blachowski
Wroclaw University of Technology, Institute of Mining Engineering, Na Grobli 15, 50-421 Wrocław, Poland
 
REFERENCES (16):
1. BLACHOWSKI J., 2008. System informacji geograficznej wałbrzyskich kopalń węgla kamiennego podstawą zwiększenia efektywności i wiarygodności badań deformacji powierzchni terenów pogórniczych, Pr. Nauk. Inst. Gór. PWroc., Stud. Mater., nr 123, Górnictwo i geologia X, nr 34, 17–27.
2. BLACHOWSKI J., STEFANIAK P., 201 Aktualizacja systemu geoinformacyjnego dawnych Wałbrzyskich Kopalń Węgla Kamiennego, Pr. Nauk. Inst. Gór. PWroc., Stud. Mater., nr 135, Górnictwo i geologia XVIII, nr 42, 5–21.
3. BLACHOWSKI J., MILCZAREK W., CACOŃ S., 2010. Project of a rock mass surface deformation monitoring system in the Walbrzych coal basin, Acta Geodynamica et Geomaterialia, Vol. 7, No 3, 349–354.
4. CHOI J.K., KIM K.D., LEE S., WON J.S., 2010. Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea, Environ Earth Sci 59(5):1009–1022.
5. DJAMALUDDIN I., MITANI Y., ESAKI T., 2011. Evaluation of ground movement and damage to structured from Chinese coal mining using a new GIS coupling model, International Journal of Rock Mechanics and Mining Sciences, Vol. 48, 3, 380–393.
6. GRAMACKI J., GRAMACKI A., 2008. Wybrane metody redukcji wymiarowości danych oraz ich wizualizacji, XIV Konferencja PLOUG, Szczyrk.
7. GUO D., CHEN J., MACEACHREN A. M., LIAO K., 2006. A Visualization System for Spatio-Temporal and Multivariate Patterns (VIS- STAMP), IEEE Transactions on Visualization and Computer Graphics, 12(6), 1461–1474.
8. KOHONEN T., HYNNINEN J., KANGAS J., LAAKSONEN J., 1996. SOM_PAK: The Self-Organizing Map Program Package, Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02150 Espoo, Finland.
9. KOHONEN T., 2001. Self-Organizing Maps. 3rd Edition. Springer.
10. KOWALSKI A. (red.), 2000. Eksploatacja górnicza a ochrona powierzchni. Doświadczenia z wałbrzyskich kopalń, Główny Instytut Górnictwa, Katowice.
11. OH H.J., LEE S., 2010. Assessment of ground subsidence using GIS and the weights-of-evidence model, Engineering Geology, Vol. 115, 1–2, 36–48.
12. OH H.J., AHN S.Ch., CHOI J.K., LEE S., 2011. Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines, Environ Earth Sci 64:347–358.
13. STEFANIAK P., ZIMROZ R., 20 Multivariate diagnostic data analysis from gearboxes of spatial distributed conveying system, Interdisciplinary Topics in Mining and Geology, edited by J. Drzymala and W. Ciezkowski, Oficyna Wydawnicza Politechniki Wrocławskiej.
14. SZUSTALEWICZ A., 2002. Visualisation of multivariate data using parallel coordinate plots and Kohonen’s SOM’s. Which is better? Advanced Computer Systems, The Springer International Series in Engineering and Computer Science, Vol. 664, pp. 89-98.
15. TOMLIN C. D., 2006. Cartographic Modeling, In: Encyclopedia of Geographic Information Science. Sage.
16. ZAHIRI H., PALAMARA D.R., FLENTJE P., BRASSINGTON G.M., BAAFI E., 2006. A GIS-based weights-of-evidence model for mapping cliff instabilities associated with mine subsidence. Environ Geol. 51(3), 377–386.
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