Application of GIS spatial regression methods in assessment of land subsidence in complicated mining conditions: case study of the Walbrzych coal mine (SW Poland)

Abstract

Land subsidence in the area of the city of Walbrzych (SW Poland) has been a consequence of underground mining of hard coal. Exploitation of multiseam deposit took place for several 100 years and ended in the late 90'ties of the twentieth century. During this time, many buildings and constructions were affected by subsidence-related damages and new landforms including mining waste dumps have developed on the surface in a consequence of underground extraction of coal. Previous studies indicated that cumulative subsidence calculated with the empirical Knothe theory reached -22 m, whereas calculations based on results of cartographic data processing showed more than -30 m (± 3 m) pointing to insufficient reliability of traditional methods when applied in complex and complicated conditions (multiseam and steeply inclined deposits). Present-day height of manmade landforms in areas affected by subsidence reaches 63 m (± 3 m). Therefore, in the presented research, a weighted spatial regression method has been proposed for analysis and modelling of mining induced land subsidence. The study concerned the former Walbrzych coal mine area and the 1886-2009 period. The subsidence modelling has been done in geographic information systems with geographically weighted regression (GWR) method that allows for spatial variability of subsidence factors. Four, out of the analysed seven, subsidence factors were identified as significant (thickness, inclination and depth of coal levels and surface slope) and used as independent (explanatory) variables in construction of the subsidence model with the GWR method. The validated model was used to estimate subsidence in up to now unmapped areas transformed by manmade landforms. The maximum predicted subsidence in these parts for the 1886-2009 period reaches -10.5 m. In the result, a spatial representation (hybrid map) of subsidence for the entire Walbrzych coal mine has been produced.

Big Data and Cloud Computing

“…Regression models describe the strength or weakness of the relationship between several variables. For example, Blachowski (2016) proposed a weighted spatial regression method that identified four significant factors inducing land subsidence: thickness, inclination, the depth of coal panels, and the slope of the surface. There are challenges in spatial data analysis using regression methods, especially for situations that are complicated enough to result in serious residuals in the regression models.…”

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