Using geographically weighted regression to explore the spatially varying relationship between land subsidence and groundwater level variations: A case study in the Choshuichi alluvial fan, Taiwan

Shang, Rong-Kang; Shiu, Yi-Shiang; Ma, Kuo‐Chen

doi:10.1109/icsdm.2011.5968998

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…The work done by Shang et al (2011) utilises GWR to explore the spatiotemporal changes based on the observed data of groundwater levels and benchmark elevations in an alluvial fan in central Taiwan and to examine the spatial heterogeneity. It revealed that occurrence and development of land subsidence is closely related to groundwater pumping, whereas Huang et al (2013) applied OLS and GWR to build models between landslide surface displacement from monitoring data and trigger factors in ArcGIS for a site in Three Gorges Reservoir area of Yangtze River in China.…”

Section: Methodsmentioning

confidence: 99%

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

Blachowski

2016

Nat Hazards

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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.

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Section: Methodsmentioning

confidence: 99%

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

Blachowski

2016

Nat Hazards

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“…Such methods are powerful for capturing the effects of spatially heterogeneous processes and can identify spatial nonstationarity in the subsidence-drawdown relation by allowing regression coefficients to vary spatially. However, previous studies on SR merely consider the relationship between subsidence and groundwater level variation at starting and ending times without considering any compaction processes (Shang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Development of a groundwater-drawdown function to estimate spatially varying land subsidence: a case study of the Choshui River basin, Taiwan

Chu¹,

Ali²,

Burbey³

Authorea

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Land subsidence caused by groundwater overexploitation is a critical global problem. Spatial distribution of land subsidence is crucial for environmental management and land planning due to the sensitive nature of land-surface gradients. Given the nonlinear relationship of subsidence and long-term drawdown from groundwater exploitation, and the heterogeneity of the aquifer in the Choshui River alluvial fan, a developed spatial regression model can effectively estimate nonlinear and spatially varying subsidence. That is, the root-mean-square-errors (RMSEs) of annual subsidences are less or equal to 0.8 cm. Considering various data inputs in the Choshui River alluvial fan, the spatial regression model offers a robust method for estimating the spatial patterns of subsidence using drawdown as observations. Results show that the largest water-level cone of depression occurs in the distal fan area. Nonetheless, the calculated subsidence bowl closely approximates the observed one located much farther inland. Without requiring extensive calibration or an elaborate numerical groundwater flow and subsidence model, the model provides reasonable and detailed patterns using a spatially varying relationship between drawdown and resulting land subsidence. Results indicate that the spatial regression model reasonably estimates the spatial distribution of the skeletal storage coefficient in the aquifer system. The large coefficient that represents inelastic compaction occurs in the inland areas, whereas the small coefficient that represents elastic compaction occurs along the coastal area. Furthermore, this model is relevant for water policy or land subsidence regulation.

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Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud

Yang

Chen

2017

Big Data Analytics for Sensor-Network Collected Intelligence

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Using geographically weighted regression to explore the spatially varying relationship between land subsidence and groundwater level variations: A case study in the Choshuichi alluvial fan, Taiwan

Cited by 7 publications

References 12 publications

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

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

Development of a groundwater-drawdown function to estimate spatially varying land subsidence: a case study of the Choshui River basin, Taiwan

Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud

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