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唐山市地质灾害综合防灾研究
引用本文:朱庆杰,苏幼坡.唐山市地质灾害综合防灾研究[J].防灾减灾工程学报,2005,25(3):309-314.
作者姓名:朱庆杰  苏幼坡
作者单位:河北理工大学地震工程研究中心,河北,唐山,063009;河北理工大学地震工程研究中心,河北,唐山,063009
基金项目:国家计委科技攻关项目;河北省科技计划
摘    要:发生在城市的自然灾害往往表现出相互联系综合成灾的特点,综合防灾是城市防灾减灾的关键。根据唐山市的实际资料,分析了该市地质灾害的主要灾种和综合成灾特点以及控制综合成灾的主要影响因素,提出了唐山市综合成灾的模式和过程。针对各种致灾因子间关联关系的复杂性,建立了基于人工神经网络的综合成灾危险性评价模型,依据唐山市的实际资料和综合成灾过程分析结果,确定了模型因子和关联系数矩阵。应用所建的综合成灾危险性评价模型,结合城市地理信息系统图件,预测了唐山市地质灾害综合成灾危险性的分布。分析了计算结果,并对唐山市的地质灾害综合防灾工作提出了几点建议。

关 键 词:唐山  地质灾害  综合防灾研究  神经网络  灾害危险性
文章编号:1672-2132(2005)03-0309-06
收稿时间:2004-10-20
修稿时间:2005-02-21

Research on Integrated Geohazard Prevention in Tangshan City
ZHU Qing-jie,SU You-po.Research on Integrated Geohazard Prevention in Tangshan City[J].Journal of Disaster Prevent and Mitigation Eng,2005,25(3):309-314.
Authors:ZHU Qing-jie  SU You-po
Abstract:The potential geohazards threatening the Tangshan city consist of earthquake and other hazards such as karstic collapse, ground subsidence caused by coal mining, and etc. The characteristics and influence factors of main geohazards in Tangshan city are analyzed, and the integrated model and forming mechanism of geohazards are investigated. The destruction effects of geohazards in Tangshan city,are controlled by many factors, the relationship of which cannot be expressed as linear functions. Potential risks of geohazards are related to seismic movement, fracturing of bed rock, karstic collapse, site category and landform. Since quantitative relationships among these influence factors are difficult to be obtained using traditional methods, the multivariate nonlinear model based on artificial neural network is employed. With this model, the distribution of geohazard risks in Tangshan city is calculated with reasonable precision. In the calculation, the learning coefficient is taken as 1.2, the inertia coefficient as 0. 5, the number of hidden layer as 2, the number of elements as 6 in No. 1 hidden layer, and 3 in No. 2 hidden layer. The geohazard risk of Tangshan city is evaluated and the distribution of geohazard risk is investigated. The results show that the geohazard risk of Tangshan city is greater than that most other areas by 40 percent, even by 70 percent greater than some local areas. Also, it is found that the geohazard risk decreases gradually from southeast to northwest. Finally, the influences of geohazard risk distribution on city planning and engineering construction are analyzed and some suggestions are given for these purposes.
Keywords:integrated geohazard  artificial neural network  geohazard risk
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