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Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS

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Environmental Geology

An Erratum to this article was published on 07 February 2009


This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area.

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  • Atkinson PM, Tatnall ARL (1997) Neural networks in remote sensing. Int J Remote Sens 18:699–709

    Article  Google Scholar 

  • Coal Industry Promotion Board, CIPB (1997) A study on the mechanism of subsidence over abandoned mine area and the construction method of subsidence prevention. Coal Industry Promoton Board, Seoul, 97–06, pp 1–67

  • Coal Industry Promotion Board, CIPB (1999) Fundamental investigation report of the stability test for Gosari. Coal Industry Promotion Board, Seoul, 99–06, pp 7–22

  • Demuth H, Beale M, Hagan M (2005) MATLAB version; Neural network toolbox for use with Matlab, the Mathworks, p 348

  • Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8:129–130

    Article  Google Scholar 

  • Goel SC, Page CH (1982) An empirical method for predicting the probability of Chimney Cave occurrence over a mining area. Int J Rock Mech Min Sci Geomech Abstr 19:325–337

    Article  Google Scholar 

  • Kim KD, Lee S, Oh HJ, Choi JK, Won JS (2006) Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS. Environ Geol 50:1183–1191

    Article  Google Scholar 

  • Hines JW (1997) Fuzzy and neural approaches in engineering. Wiley, New York, p 209

    Google Scholar 

  • Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3/4):289–302

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3/4):171–191

    Article  Google Scholar 

  • National Coal Board (1975) Subsidence engineer’s handbook. National Coal Board Mining Department, London, p 111

    Google Scholar 

  • Paola JD, Schowengerdt RA (1995) A review and analysis of backpropagation neural networks for classificatioin of remotely sensed multi-spectral imagery. Int J Remote Sens 16:3033–3058

    Article  Google Scholar 

  • Sonmez H, Gokceoglu C, Kayabaşı A, Nefeslioğlu HA (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235

    Article  Google Scholar 

  • The Geological Society of Korea (1999) Geology of Korea. Sigma Press, Seoul, pp 550–556

    Google Scholar 

  • Tunusluoglu MC, Gokceoglu C, Sonmez H, Nefeslioglu HA (2007) An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey). Nat Hazards Earth Syst Sci 7:557–570

    Article  Google Scholar 

  • Waltham AC (1989) Ground subsidence. Blackie & Son Ltd, New York, pp 49–97

    Google Scholar 

  • Zhou W (1999) Verification of the nonparametric characteristics of backpropagation neural networks for image classification. IEEE Trans Geosci Remote Sens 37:771–779

    Article  Google Scholar 

  • Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3–4):141–158

    Article  Google Scholar 

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The authors thank the Coal Industry Promotion Board to have provided whole investigation reports and basic GIS database. This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and Technology of Korea.

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Correspondence to Saro Lee.

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Kim, KD., Lee, S. & Oh, HJ. Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environ Geol 58, 61–70 (2009).

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