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Comprehensive fuzzy evaluation model and evaluation of the karst collapse susceptibility in Zaozhuang Region, China
Authors:Keqiang He  Yuyue Jia  Bin Wang  Ronglu Wang  Huilai Luo
Institution:1. Department of Civil Engineering, Qingdao Technological University, No. 11 Fushun Road, Qingdao, 266033, Shandong, People’s Republic of China
2. Shijiazhuang University of Economics, No. 136 Huaian East Road, Shijiazhuang, 050031, Hubei, People’s Republic of China
3. Shandong Building Materials Geological Exploration Center of SINOMA, Jinan, 250000, People’s Republic of China
Abstract:Karst collapses in Zaozhuang area are the typical karst collapses induced by groundwater over-pumping in north China, which lead to severe economic loss and negative societal influence. Based on the comprehensive analysis of all natural influencing factors and human influencing factors associated with karst collapse in the Zaozhuang Shili Spring Area, China, this paper establishes karst collapse susceptibility evaluation model by means of fuzzy mathematic method, in which the Quantitative theory method was firstly applied in determination of the factor weights. The karst collapse susceptibility evaluation model with 14 evaluation indicators can be effectively used for quantitative evaluation on the karst collapse susceptibility. So, an analysis and evaluation on the karst collapse susceptibility of Zaozhuang Shili Spring Area is completed by means of karst collapse susceptibility evaluation model. The karst collapse area of Zaozhuang region is divided into very high susceptibility area, high susceptibility area, medium susceptibility area and low susceptibility area, which account for about 6.3, 5.2, 10.4 and 78.1 % of the total karst area and the karst collapse susceptibility evaluation map of Zaozhuang Shili Spring Area is completed. The results above will be helpful for the strategic planning and decision-making processes associated with exploitation of karst water resources and with prevention and control of karst collapse hazards of this region.
Keywords:
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