DocumentCode :
2526179
Title :
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
Author :
Shang, Rong-Kang ; Shiu, Yi-Shiang ; Ma, Kuo-Chen
Author_Institution :
Dept. of Geogr., Nat. Taiwan Univ., Taipei, Taiwan
fYear :
2011
fDate :
June 29 2011-July 1 2011
Firstpage :
21
Lastpage :
25
Abstract :
Land subsidence mainly caused by excessive extraction of groundwater is a growing and worldwide problem. Sustained decline in groundwater level has a direct impact on land surface elevation. However, the impact may not be consistent across subsidence areas. The spatially varying relationship between land subsidence and groundwater level variations remains unclear. This study explores the spatio-temporal changes based on the observed data of groundwater levels and benchmark elevations from 2002 to 2009 in the Choshuichi alluvial fan of central Taiwan and examines the spatial heterogeneity with geographically weighted regression (GWR). The results reveal that the occurrence and development of land subsidence is closely related to the groundwater pumping. Moreover, the influence of groundwater level on land subsidence is more significant in the inland area. The study can help to predict the land subsidence caused by the overdraft of groundwater and provide an explicit strategy for groundwater resource management.
Keywords :
geomorphology; groundwater; regression analysis; water conservation; water resources; Choshuichi alluvial fan; Taiwan; geographically weighted regression; groundwater level variations; groundwater pumping; groundwater resource management; land subsidence; land surface elevation; Aquaculture; Biological system modeling; Global Positioning System; Land surface; Loading; Predictive models; Water resources; Taiwan; geographically weighted regression; groundwater level; land subsidence; spatial heterogeneity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2011 IEEE International Conference on
Conference_Location :
Fuzhou
Print_ISBN :
978-1-4244-8352-5
Type :
conf
DOI :
10.1109/ICSDM.2011.5968998
Filename :
5968998
Link To Document :
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