偏最小二乘回归神经网络的矿坑涌水量预测

偏最小二乘回归神经网络的矿坑涌水量预测

陈南祥^1,2,曹连海²,李梅¹,黄强¹

1.西安理工大学水利水电学院，陕西西安 710048；2.华北水利水电学院岩土工程系，河南郑州 450008

收稿日期:2005-01-28 修回日期:1900-01-01 出版日期:2005-11-26 发布日期:2005-11-26
通讯作者: 陈南祥

Forecasting Water Yield of Mine with the Partial LeastSquare Method and Neural Network

CHEN Nan-xiang^1,2, CAO Lian-hai²,LI Mei¹,HUANG Qiang¹

1. Institute of Water Resources and HydroElectric Engineering, Xi’an University of Technology, Xi’an 710048,China; 2. Department of Geotechnical Engineering, North China Institute of Water Conservancy and Hydroelectric Power, Zhengzhou 450008,China

Received:2005-01-28 Revised:1900-01-01 Online:2005-11-26 Published:2005-11-26
Contact: CHEN Nanxiang

摘要/Abstract

摘要： 影响矿坑充水的因素多且复杂，矿坑涌水量预测模型主要考虑降水、地表水、引水灌溉等影响因素，因变量和自变量的关系比较复杂。将偏最小二乘回归与神经网络耦合，建立了矿坑涌水预报模型。模型将自变量利用偏最小二乘回归处理，提取对因变量影响强的成分，既可以克服变量之间的相关性问题，又可以降低神经网络的输入维数，并能较好地解决非线性问题，提高了模型的学习能力和表达能力。以河南鹤壁八矿涌水量为例，建立了基于偏最小二乘回归和神经网络耦合的矿坑涌水量预测模型。计算验证表明,该类模型具有较高的预报精度和推广应用价值。

关键词: 矿坑涌水量, 偏最小二乘回归, 神经网络, 预报模型

Abstract: There are many and complex factors affecting the gushing water in pit. The forecasting model of water yield of mine mostly takes into account of precipitation, surface water, irrigation and the relation of following variable and independent variable. The authors establish the forecasting model for water yield of mine, combining neural network model with the partial least square method. To deal with independent variables by the partial least square method can not only solve the relationship between independent variables but also to reduce the input dimensions in neural network model. And when the neural network is applied,it can solve the nonlinear problem better，and advance study and expression ability of the model. As the example of water yield of mine in Eighth mine, Hebi City, Henan Province, the model of water yield of mine,coupled with partial least square method and neural network, is founded and the case study shows it has rather high forecasting precision and the extending application value.

Key words: water yield of mine, partial least square method, neural network, forecasting model

中图分类号:

P641.41

陈南祥,曹连海,李梅,黄强. 偏最小二乘回归神经网络的矿坑涌水量预测[J]. J4, 2005, 35(06): 766-0770.

CHEN Nan-xiang, CAO Lian-hai,LI Mei,HUANG Qiang. Forecasting Water Yield of Mine with the Partial LeastSquare Method and Neural Network[J]. J4, 2005, 35(06): 766-0770.

参考文献

相关文章 15

[1]	张代磊, 黄大年, 张冲. 基于遗传算法优化的BP神经网络在密度界面反演中的应用[J]. 吉林大学学报(地球科学版), 2017, 47(2): 580-588.
[2]	王宇, 卢文喜, 卞建民, 侯泽宇. 三种地下水位动态预测模型在吉林西部的应用与对比[J]. 吉林大学学报(地球科学版), 2015, 45(3): 886-891.
[3]	杜润林, 刘展. 基于粒子群优化的细胞神经网络油气重力异常信息提取[J]. 吉林大学学报(地球科学版), 2015, 45(3): 926-933.
[4]	潘保芝, 石玉江, 蒋必辞, 刘丹, 张海涛, 郭宇航, 杨小明. 致密砂岩气层压裂产能及等级预测方法[J]. 吉林大学学报(地球科学版), 2015, 45(2): 649-654.
[5]	刘博, 肖长来, 梁秀娟. SOM-RBF神经网络模型在地下水位预测中的应用应用[J]. 吉林大学学报(地球科学版), 2015, 45(1): 225-231.
[6]	刘贺，张弘强，刘斌. 基于粒子群优化神经网络算法的深基坑变形预测方法[J]. 吉林大学学报(地球科学版), 2014, 44(5): 1609-1614.
[7]	鲁功达,晏鄂川,王环玲,王雪明,谢良甫. 基于岩石地质本质性的碳酸盐岩单轴抗压强度预测[J]. 吉林大学学报(地球科学版), 2013, 43(6): 1915-1921.
[8]	徐黎明，王清,陈剑平，潘玉珍. 基于BP神经网络的泥石流平均流速预测[J]. 吉林大学学报(地球科学版), 2013, 43(1): 186-191.
[9]	周晓华, 林君, 陈祖斌, 焦健, 郭同健. 改进的神经网络反演微动面波频散曲线[J]. J4, 2011, 41(3): 900-906.
[10]	朴金石, 殷琨, 范黎明. 利用神经网络法预测风动潜孔锤钻速[J]. J4, 2009, 39(5): 882-886.
[11]	张晨，陈剑平,肖云华. 基于神经网络对有限元强度折减法分析[J]. J4, 2009, 39(1): 114-0118.
[12]	郄瑞卿，薛林福，王满，王丽华. SOFM储层综合评价方法及其在延吉盆地的应用[J]. J4, 2009, 39(1): 168-0174.
[13]	秦胜伍，陈剑平. 隧道围岩压力的神经网络时间序列分析[J]. J4, 2008, 38(6): 1005-1009.
[14]	郝立波，蒋艳明，陆继龙，孙淑梅，白荣杰. 利用多目标地球化学数据识别第四纪沉积物类型--基于概率神经网络方法[J]. J4, 2008, 38(6): 1081-1084.
[15]	邱道宏，陈剑平，阙金声，安鹏程. 基于粗糙集和人工神经网络的洞室岩体质量评价[J]. J4, 2008, 38(1): 86-0091.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed