| 李立伟,宋小军.基于MODFLOW的BP神经网络模型对地面沉降的模拟研究[J].矿产勘查,2010,1(6):569-575 |
| 基于MODFLOW的BP神经网络模型对地面沉降的模拟研究 |
| Simulating research on land Subsidence with BP neural network model based on MODFLOW in Ninghe |
| 投稿时间：2010-09-10 |
| DOI： |
| 中文关键词: 地面沉降 地下水开采 MODFLOW数值模型 BP神经网络 |
| 英文关键词: land subsidence groundwater exploration MODFLOW numerical simulation BP Neural Network Model |
| 基金项目: |
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| 摘要点击次数: 2034 |
| 全文下载次数: 1108 |
| 中文摘要: |
| 针对区域性地面沉降问题,文章运用MODFLOW软件中的水井子程序包,在地面沉降监测点处设置了虚拟水位观测井,以虚拟水井模拟的年均水位和统计的年开采量作为输入,各监测点的年沉降量作为输出,引入并构建了BP神经网络模型。对区内6个控沉点的沉降量预测结果表明,随着开采量的减少和水位的回升,地面沉降趋于缓和。同时,作者对BP神经网络模型的物理意义进行了有益的探索性研究。 |
| 英文摘要: |
| In order to control land subsidence efficiently, the article has arranged the dummy water level observation well right on the monitoring point using the well program package of MODFLOW, input the annul water level simulated by the dummy well and the statistical annul produced quantity, output the annul land subsidence of each monitoring points, and established the BP Neural Network Model. From the predicting outcome of 6 sites, it has been found that the deep under-groundwater level is rising, the funnel area is decreasing, and the annul land subsidence tendency is tempered. At the same time, the author explored the physical significance of the BP neural network model. |
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