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| 人工神经网络在天津市区地面沉降预测中的应用 | |
| 引用本文： | 李涛,潘云,娄华君,李波,王宏,邹立芝.人工神经网络在天津市区地面沉降预测中的应用[J].地质通报,2005,24(7):677-681. |
| 作者姓名： | 李涛 潘云 娄华君 李波 王宏 邹立芝 |
| 作者单位： | 1. 吉林大学环境与资源学院,吉林,长春,130026 2. 首都师范大学资源环境与地理信息系统北京市重点实验室,北京,100037 3. 中国科学院地理科学与资源研究所,北京,100101 4. 山东省莱阳市广播电视局,山东,莱阳,265200 5. 中国地质科学院,北京,100037 |
| 基金项目： | 中国科学院知识创新工程资助项目(KZCX-SW-317-01)成果。 |
| 摘 要： | 在分析天津市区地面沉降特点的基础上，结合人工神经网络原理，选择1961-1980年的天津市区降水量、地下水开采量、前年沉降量、固结度作为训练样本的输入量，以这20年的地面沉降量作为输出量，用贝叶斯正则化算法训练BP网络，得到沉降的仿真模型。并把1981-1993年的资料用来进行预测检验，结果表明这是一种比较理想的地面沉降预测方法。最后在不同的降水量保证率下，预测了到2010年天津市区地面沉降的情况。 |
| 关 键 词： | 地面沉降 人工神经网络 BP算法 降水量保证率 |
| 文章编号： | 1671-2552(2005)07-0677-05 |
| 修稿时间： | 8/7/2004 12:00:00 AM |
Application of the artificial neural network in land subsidence prediction in the urban area of Tianjin Municipality, China. |
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| LI Tao,PAN Yun,LOU Huajun,WANG Hong,ZOU Lizhi. Environment and Resource College,Jilin University,Changchun,Jilin,Chin,. Key Lab. of Resource Environment and GIS of Beijing,Capital Normal University,Beijing,Chin,. Institute of Geographical Sciences and Natural Resources,Chinese Academy of Sciences,Beijing,Chin,. Laiyang Radio and TV.Application of the artificial neural network in land subsidence prediction in the urban area of Tianjin Municipality, China.[J].Geologcal Bulletin OF China,2005,24(7):677-681. | |
| Authors: | LI Tao PAN Yun LOU Huajun WANG Hong ZOU Lizhi Environment and Resource College Jilin University Changchun Jilin Chin Key Lab of Resource Environment and GIS of Beijing Capital Normal University Beijing Chin Institute of Geographical Sciences and Natural Resources Chinese Academy of Sciences Beijing Chin Laiyang Radio and TV |
| Institution: | LI Tao,PAN Yun,LOU Huajun,WANG Hong,ZOU Lizhi. Environment and Resource College,Jilin University,Changchun,Jilin,Chin,. Key Lab. of Resource Environment and GIS of Beijing,Capital Normal University,Beijing,Chin,. Institute of Geographical Sciences and Natural Resources,Chinese Academy of Sciences,Beijing,Chin,. Laiyang Radio and TV Department,Shandong Province,Laiyang,Shandong,China. Chinese Academy of Geological Sciences,Beijing,China |
| Abstract: | On the basis of an analysis of the characteristics of subsidence,combined with the principle of the artificial neural network, the precipitation, groundwater yield, drawdown of the previous year and degree of consolidation between 1961 and 1980 in the urban district of Tianjin were taken as training net sample inputs and the subsidence over the 20 years as outputs. The subsidence simulation model was constructed after training of the back-propagation network with the Bayesian method. Then the data of 1981 to 1993 were used to check the model. The results indicate that this method with the artificial neural network is an ideal one to predict subsidence. At last the subsidence until 2010 of the urban district of Tianjin was predicted using different levels of precipitation assurance. |
| Keywords: | ground subsidence artificial neural network back-propagation method precipitation assurance |
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