| ANN技术在地下水含水量预测建模中的研究与应用 |
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| 引用本文： | 宋洪伟 刘继朝 石建省 张翼龙 夏,凡 苗青壮.ANN技术在地下水含水量预测建模中的研究与应用[J].中国地质,2012,39(4):1081-1086. |
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| 作者姓名： | 宋洪伟 刘继朝 石建省 张翼龙 夏 凡 苗青壮 |
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| 作者单位： | 1. 中国地质科学院水文地质环境地质研究所,河北石家庄,050061
2. 石家庄经济学院,河北石家庄,050031 |
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| 基金项目： | 科研基金，中国地质调查局地调项目，* |
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| 摘 要： | 将人工神经网络(ANN)技术引入到地下水含水量预测工作,以华北平原和河套平原为试验场,以若干已知钻孔为验证,采用激电和电阻率测深等地面物探方法获取视电阻率ρS、视极化率ηS、半衰时Th、衰减度D和偏离度σ等参数为输入神经元对单孔单位涌水量建立人工神经网络预测模型。同时,为消除不同地区矿化度的影响,通过实验对比引入综合参数T",改良了输入神经元的配比。最终建立以半衰时Th、衰减度D、偏离度σ和综合参数T"为输入神经元的含水量预测模型,进一步提高了预测精度。通过检验,发现所建立的模型对平原地区进行含水量的定量预测有着较好的效果,为含水量预测工作研究与发展带来了新理念、打开了新思路。
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| 关 键 词： | ANN 涌水量 矿化度 偏离度 预测模型 |
The study and application of ANN to the Modeling of underground water content forecast |
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| SONG Hong-wei,SHI Jian-sheng,LIU Ji-chao,ZHANG Yi-long,XIA Fan,MIAO Qing-zhuang.The study and application of ANN to the Modeling of underground water content forecast[J].Chinese Geology,2012,39(4):1081-1086. |
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| Authors: | SONG Hong-wei SHI Jian-sheng LIU Ji-chao ZHANG Yi-long XIA Fan MIAO Qing-zhuang |
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| Institution: | 1.Institute of Hydrogeology and Environmental Geology,CAGS,Shijiazhuang 050061,Hebei,China;2.Shijiazhuang University of Economics,Shijiazhuang 050031,Hebei,China) |
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| Abstract: | This paper has introduced the technology of artificial network into the modeling of underground water content forecast.Hetao plain and Huabei plain were chosen as the testing ground with a number of known local agro-wells as the verification sites.Induced Polarization(IP) and resistivity sounding and other surface geophysical methods were used to construct the artificial neural network(ANN) model based on such parameters as apparent resistivity,polarization rate,half-life,decay rate and rate of deviation in the relevance.Then,the comprehensive parameter was added to improve the inputting neurons.Finally,the quantitative prediction model of the water content was establiehed.The results of mean-variance test show that this technique has a good effect in the plain area.The study has provided a new concept and a new idea for the forecasting work in hydrogeological exploration. |
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| Keywords: | ANN water inflow mineralization deviation forecast model |
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