程潮铁矿大气降水—涌水量统计预测分析
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摘要
程潮铁矿井下涌水来源相对较简单,地表水系不发达,由于地下开采的不断疏干排水,矿区地下水的静储量逐渐减少,大气降水的渗透补给成为矿坑充水的主要来源。根据程潮铁矿的开采技术条件,防治地下水害的重要技术措施是研究井下涌水量与大气降水的相互关系和影响。
系统统计了程潮铁矿近年降水量及井下涌水量资料,运用多元统计相关分析技术,探讨了井下涌水量相对大气降水量的滞后时间关系,研究表明,大气降水渗透补给与矿坑涌水之间存在滞后时间,在汛期延迟时间持续较久,是影响涌水量主要因素。在此基础上进一步分别研究了正常降水和汛期降水对井下涌水量变化的时间序列特征和规律,利用传递函数模型较清晰地描述了大气降水-井下涌水系统变化规律,并对程潮铁矿2010年雨季大气降水量和涌水量进行了统计预测分析。针对统计数据的波动性大、趋势变化不稳定、季节影响因素多、历史数据不完整等特点,在传递函数残差改进分析的基础上建立了基于二阶有效度的BP-传递函数的组合预测模型。模型预测结果揭示涌水量的主要来源为大气降雨,且降水量增大时会在一段时期内持续影响涌水量的变化。因此,在预防透水事故的发生时,对其措施的分析和应急方案的制定成为程潮铁矿安全工作的主要内容。
The hydrology geology condition of Chenchao Iron Mine is simple, and the ground surface water system is undeveloped. With the increase of mineral drainage, the ground water static reserves reduces gradually, so the main source of water-filled pit becoming the infiltration of rainfall recharge. The most important task of prevention in Chengchao Iron Mine is the precipitation analysis and the prediction of water inflow into underground water gushing.
The general statistics of rainfall and discharge this year in Chengchao Iron Mine shows that the mine water inflow was delayed by the precipitation infiltration of supplies through correlation analysis, the reason of the discharge affecting was that the flood season was sustained by a long time. On the basis of the relationship between atmospheric precipitation and water inflow, a statistical prediction of atmospheric precipitation and discharge was conducted on 2010 in Chengchao Iron Mine. As the feature is that the statistical data was easy to fluctuate and the transformation trends were unstable, the seasonal factors and the incompletion of the historical data existed, the analysis of effectiveness with the BP-order transfer function of the combination forecasting model was established based on the residual improvement in the transfer function. The model prediction results reveal that the main source of water inflow was the rainfall, which would continue the change in discharge over a period of time when the precipitation increased. Therefore, when preventing the flooding accident from being occurred, the analysis of the prevention method and the establishment of the emergency project have become the main content of security in Chengchao Iron Mine.
系统统计了程潮铁矿近年降水量及井下涌水量资料,运用多元统计相关分析技术,探讨了井下涌水量相对大气降水量的滞后时间关系,研究表明,大气降水渗透补给与矿坑涌水之间存在滞后时间,在汛期延迟时间持续较久,是影响涌水量主要因素。在此基础上进一步分别研究了正常降水和汛期降水对井下涌水量变化的时间序列特征和规律,利用传递函数模型较清晰地描述了大气降水-井下涌水系统变化规律,并对程潮铁矿2010年雨季大气降水量和涌水量进行了统计预测分析。针对统计数据的波动性大、趋势变化不稳定、季节影响因素多、历史数据不完整等特点,在传递函数残差改进分析的基础上建立了基于二阶有效度的BP-传递函数的组合预测模型。模型预测结果揭示涌水量的主要来源为大气降雨,且降水量增大时会在一段时期内持续影响涌水量的变化。因此,在预防透水事故的发生时,对其措施的分析和应急方案的制定成为程潮铁矿安全工作的主要内容。
The hydrology geology condition of Chenchao Iron Mine is simple, and the ground surface water system is undeveloped. With the increase of mineral drainage, the ground water static reserves reduces gradually, so the main source of water-filled pit becoming the infiltration of rainfall recharge. The most important task of prevention in Chengchao Iron Mine is the precipitation analysis and the prediction of water inflow into underground water gushing.
The general statistics of rainfall and discharge this year in Chengchao Iron Mine shows that the mine water inflow was delayed by the precipitation infiltration of supplies through correlation analysis, the reason of the discharge affecting was that the flood season was sustained by a long time. On the basis of the relationship between atmospheric precipitation and water inflow, a statistical prediction of atmospheric precipitation and discharge was conducted on 2010 in Chengchao Iron Mine. As the feature is that the statistical data was easy to fluctuate and the transformation trends were unstable, the seasonal factors and the incompletion of the historical data existed, the analysis of effectiveness with the BP-order transfer function of the combination forecasting model was established based on the residual improvement in the transfer function. The model prediction results reveal that the main source of water inflow was the rainfall, which would continue the change in discharge over a period of time when the precipitation increased. Therefore, when preventing the flooding accident from being occurred, the analysis of the prevention method and the establishment of the emergency project have become the main content of security in Chengchao Iron Mine.
引文
[1]王学杰.马路坪矿段深部开采涌水量预测及防治研究[D].湖南:中南大学,2004.
[2]李树文.矿井涌水量预测的灰色数值模型研究[J].科技情报开发与经济,2007(4):185-187.
[3]李文昌.试论矿井突(涌)水系统及其研究方法[J].华北科技学院学报,2008,5(2):64-67.
[4]毛艺萍.统计预测模型的算法及新发展[D].广东:暨南大学,2006.
[5]陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008.
[6]冯文权,茅奇,周毓萍.经济预测与决策技术(第四版)[M].武汉:武汉大学出版社,2002.
[7]刘思峰,党耀国,方志耕.灰色系统理论及其应用(第五版)[M].北京:科学出版社,2010.
[8] Hou T H, Su C H, Chang H Z.Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process[J].Expert Systems with Applications, 2008,34: 427-436.
[9]张桂喜,张立平.预测与决策概论[M].北京:首都经济贸易大学出版社,2006.
[10]宁宣熙,刘思峰.管理预测与决策方法[M].北京:科学出版社,2003.
[11]李正龙.经济预测与决策方法[M].合肥:安徽大学出版社,2002.
[12] Armstrong J S, Collopyf F, Yokum J T.Decomposition by causal forces: a procedure for forecasting complex time series[J].International Journal of Forecasting, 2005, 21(1): 25-36.
[13]孙祝岭,徐晓岭.数理统计[M].北京:高等教育出版社,2009.
[14] Wu Xiaoyi and Wang Jingwen, The research of Newton's interpolation applied to the calculating the calculating the theoretical plates of rectifying column[J].Computers and Applied Chenistry, 2009,26(6): 770-772.
[15]吴载斌.统计预测方法综合研究与计算机实现[D].广东:暨南大学,2003.
[16] Guo R.Modeling imperfectly repaired system data via grey differential equations with unequal-gapped times[J] . Reliability Engineering and System Safety,2007, 92(3): 378-391.
[18]吕广忠,石银堂.东河湾铁矿井下涌水治理研究[J].金属矿山,2003(3):13-14
[19] Song H, Witts F, Jensent C.Tourism forecasting: accuracy of alternative econometric models[J].International Journal of Forecasting, 2003, 19(1): 123-141.
[20]王振龙,胡永宏.应用时间序列分析[M].北京:科学出版社,2007.
[21]肖枝洪,郭明月.时间序列分析与SAS应用[M].武汉:武汉大学出版社,2009.
[22]王沁.时间序列分析及其应用[M].成都:西南交通大学出版社,2008.
[23]类淑河.利用传递函数噪声模型表达经济变量间的动态影响机制[J].数理统计与管理,2004,(1):20-22.
[24] Deepesh Machwal, Madank J H A.Comparative evaluation of statistical tests for time series analysis: application to hydrological time series[J].Hydrological Sciences Journal, 2008, 53(3): 60-63.
[25]苗敬毅.中国固定资产投资与经济增长的传递函数模型[J].生产力研究,2006(4):12-13.
[26] Willam W.S.Wei.Time series analysis: univariate and multivariate methods[M].北京:中国人民大学出版社,2009.
[27]张善文,雷英杰,冯有前.MATLAB在时间序列分析中的应用[M].西安:西安电子科技大学出版社,2007.
[28]赵玉杰,师荣光,高怀友,等.基于MATLAB6.0的BP人工神经网络的土壤环境质量评价方法研究[J].农业环境科学学报,2006,25(1):186-189.
[29] Holt C C . Forecasting seasonals and trends by exponentially weighted moving averages[J].International Journal of Forecasting, 2004, 20(1): 5-10.
[30]高晓,李坷凌,陈蕾.小型煤矿矿井涌水量预测方法探讨[J].地下水,2009,1:55-56.
[31]吴起,蒋军成.基于BP神经网络技术的实验数据分析处理[J].中国安全科学学报,2006,16(1):39-43.
[32] Taylor J W, Bunn D W.Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: a simulation study[J].International Journal of Forecasting, 1999, 15(3): 325-339.
[33]程松林,任利伟.基于灰色和神经网络的最优组合预测分析[J].上海电机学院学报,2009,6:84-86.
[34] Hanias M R, Karras D A.On efficient multistep non-linear time series prediction in chaotic diode resonator circuits by optimizing the combination of non-linear time series analysis and neural networks[J].Engineering Applications of Artificial Intelligence , 2009, 22(1): 43-46.
[35]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2009.
[36] Koehler A B, Snyder R D, Ord J K.Forecasting models and prediction intervals for the multiplicative Holt-winters method [J].International Journal of Forecasting, 2001, 17(2): 269-286.
[37]赵雯雯,叶义成,邢冬梅.基于预测有效度的矿山安全组合预测模型研究[J].矿山机械,2010,36(6):54-57.
[38] Clement Michael P, Hendry David F.Forecasting economic time series[M].北京:北京大学出版社,2008.
[39]王会青,王婷,谷志红.基于灰色神经网络法的高负荷预测[J].华东电力,2005,33(4):11-13.
[40] Liu Zude.Safety investment and the optimal safety level[J].Safety and Environmental Engineering, 2000, 4(2): 26-28.
[41]樊洁,张新元,严广乐.多模型组合预测技术在渭南地税中的应用[J].科技与管理,2009,11(2):16-18.
[42]韩冬梅,牛文清,杨荣.线性与非线性最优组合预测方法的比较研究[J].情报科学,2007,25(11):22-24.
[43] Zhang Y, Jansen B.J, Spink A.Time series analysis of a Web search engine transaction log[J].Information Processing & Management, 2009, 45(2): 63-66.
[44]陈华友.基于预测有效度的组合预测模型求解方法研究[J].安徽大学学报(自然科学版),2003,27(3):6-11.
[45]罗云,吕海燕,白福利.事故分析预测与事故管理[M].北京:化学工业出版社,2005.
[46]李莹莹,叶义成,吕垒,黄军.矿业系统安全事故周期分析及预测研究[J].工业安全与环保,2010,36(6):54-57.
[47]秦德智,刘新卫.管理预测定量方法与模型[M].北京:科学出版社,2007,1
[48]袁小明.安庆铜矿水文地质特征及矿坑水害防治[J].有色金属设计,2003,01,43-45.
[49]杨潮进.地采金属矿山透水事故预防探讨[J].有色金属(矿山部分),2008,60(2):13-15.
[50] Xiang Yansun.Introduction on the Development Safety Science and Technology in China These Years[C] . 2002’International Symposium on Mining Science and Safety Technology.Beijing: Science Press, 2002: 358-361.
[51]景国勋,施式亮等.系统安全评价与预测[M].江苏:中国矿业大学出版社,2009.
[52] Xin Song, Cheng Weimin, Wang Chunyuan.Marginal Analysis Technology of Best Safety Investment[C] . Proceedings of 2006(Shenyang)International Colloquium on Safety Science and Technology.Shenyang:Liaoning Science and Technology Publishing House, 2006(26): 52-56.
[53]侯江峰.矿井透水事故预防探讨[J].山西煤炭,2010,30(3):65-67.
[54]邢冬梅,叶义成,赵雯雯.我国矿山透水事故的统计分析及安全管理对策[J].金属矿山,2010(6):76-78.
[55]黄景坡,王为标.浅析贾汪矿区降雨量与涌水量的关系[J].煤炭科技,2006,2(1):9-11.
[56]隋鹏程,陈宝智,隋旭.安全原理[M].北京:化学工业出版社,2005.
[2]李树文.矿井涌水量预测的灰色数值模型研究[J].科技情报开发与经济,2007(4):185-187.
[3]李文昌.试论矿井突(涌)水系统及其研究方法[J].华北科技学院学报,2008,5(2):64-67.
[4]毛艺萍.统计预测模型的算法及新发展[D].广东:暨南大学,2006.
[5]陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008.
[6]冯文权,茅奇,周毓萍.经济预测与决策技术(第四版)[M].武汉:武汉大学出版社,2002.
[7]刘思峰,党耀国,方志耕.灰色系统理论及其应用(第五版)[M].北京:科学出版社,2010.
[8] Hou T H, Su C H, Chang H Z.Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process[J].Expert Systems with Applications, 2008,34: 427-436.
[9]张桂喜,张立平.预测与决策概论[M].北京:首都经济贸易大学出版社,2006.
[10]宁宣熙,刘思峰.管理预测与决策方法[M].北京:科学出版社,2003.
[11]李正龙.经济预测与决策方法[M].合肥:安徽大学出版社,2002.
[12] Armstrong J S, Collopyf F, Yokum J T.Decomposition by causal forces: a procedure for forecasting complex time series[J].International Journal of Forecasting, 2005, 21(1): 25-36.
[13]孙祝岭,徐晓岭.数理统计[M].北京:高等教育出版社,2009.
[14] Wu Xiaoyi and Wang Jingwen, The research of Newton's interpolation applied to the calculating the calculating the theoretical plates of rectifying column[J].Computers and Applied Chenistry, 2009,26(6): 770-772.
[15]吴载斌.统计预测方法综合研究与计算机实现[D].广东:暨南大学,2003.
[16] Guo R.Modeling imperfectly repaired system data via grey differential equations with unequal-gapped times[J] . Reliability Engineering and System Safety,2007, 92(3): 378-391.
[18]吕广忠,石银堂.东河湾铁矿井下涌水治理研究[J].金属矿山,2003(3):13-14
[19] Song H, Witts F, Jensent C.Tourism forecasting: accuracy of alternative econometric models[J].International Journal of Forecasting, 2003, 19(1): 123-141.
[20]王振龙,胡永宏.应用时间序列分析[M].北京:科学出版社,2007.
[21]肖枝洪,郭明月.时间序列分析与SAS应用[M].武汉:武汉大学出版社,2009.
[22]王沁.时间序列分析及其应用[M].成都:西南交通大学出版社,2008.
[23]类淑河.利用传递函数噪声模型表达经济变量间的动态影响机制[J].数理统计与管理,2004,(1):20-22.
[24] Deepesh Machwal, Madank J H A.Comparative evaluation of statistical tests for time series analysis: application to hydrological time series[J].Hydrological Sciences Journal, 2008, 53(3): 60-63.
[25]苗敬毅.中国固定资产投资与经济增长的传递函数模型[J].生产力研究,2006(4):12-13.
[26] Willam W.S.Wei.Time series analysis: univariate and multivariate methods[M].北京:中国人民大学出版社,2009.
[27]张善文,雷英杰,冯有前.MATLAB在时间序列分析中的应用[M].西安:西安电子科技大学出版社,2007.
[28]赵玉杰,师荣光,高怀友,等.基于MATLAB6.0的BP人工神经网络的土壤环境质量评价方法研究[J].农业环境科学学报,2006,25(1):186-189.
[29] Holt C C . Forecasting seasonals and trends by exponentially weighted moving averages[J].International Journal of Forecasting, 2004, 20(1): 5-10.
[30]高晓,李坷凌,陈蕾.小型煤矿矿井涌水量预测方法探讨[J].地下水,2009,1:55-56.
[31]吴起,蒋军成.基于BP神经网络技术的实验数据分析处理[J].中国安全科学学报,2006,16(1):39-43.
[32] Taylor J W, Bunn D W.Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: a simulation study[J].International Journal of Forecasting, 1999, 15(3): 325-339.
[33]程松林,任利伟.基于灰色和神经网络的最优组合预测分析[J].上海电机学院学报,2009,6:84-86.
[34] Hanias M R, Karras D A.On efficient multistep non-linear time series prediction in chaotic diode resonator circuits by optimizing the combination of non-linear time series analysis and neural networks[J].Engineering Applications of Artificial Intelligence , 2009, 22(1): 43-46.
[35]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2009.
[36] Koehler A B, Snyder R D, Ord J K.Forecasting models and prediction intervals for the multiplicative Holt-winters method [J].International Journal of Forecasting, 2001, 17(2): 269-286.
[37]赵雯雯,叶义成,邢冬梅.基于预测有效度的矿山安全组合预测模型研究[J].矿山机械,2010,36(6):54-57.
[38] Clement Michael P, Hendry David F.Forecasting economic time series[M].北京:北京大学出版社,2008.
[39]王会青,王婷,谷志红.基于灰色神经网络法的高负荷预测[J].华东电力,2005,33(4):11-13.
[40] Liu Zude.Safety investment and the optimal safety level[J].Safety and Environmental Engineering, 2000, 4(2): 26-28.
[41]樊洁,张新元,严广乐.多模型组合预测技术在渭南地税中的应用[J].科技与管理,2009,11(2):16-18.
[42]韩冬梅,牛文清,杨荣.线性与非线性最优组合预测方法的比较研究[J].情报科学,2007,25(11):22-24.
[43] Zhang Y, Jansen B.J, Spink A.Time series analysis of a Web search engine transaction log[J].Information Processing & Management, 2009, 45(2): 63-66.
[44]陈华友.基于预测有效度的组合预测模型求解方法研究[J].安徽大学学报(自然科学版),2003,27(3):6-11.
[45]罗云,吕海燕,白福利.事故分析预测与事故管理[M].北京:化学工业出版社,2005.
[46]李莹莹,叶义成,吕垒,黄军.矿业系统安全事故周期分析及预测研究[J].工业安全与环保,2010,36(6):54-57.
[47]秦德智,刘新卫.管理预测定量方法与模型[M].北京:科学出版社,2007,1
[48]袁小明.安庆铜矿水文地质特征及矿坑水害防治[J].有色金属设计,2003,01,43-45.
[49]杨潮进.地采金属矿山透水事故预防探讨[J].有色金属(矿山部分),2008,60(2):13-15.
[50] Xiang Yansun.Introduction on the Development Safety Science and Technology in China These Years[C] . 2002’International Symposium on Mining Science and Safety Technology.Beijing: Science Press, 2002: 358-361.
[51]景国勋,施式亮等.系统安全评价与预测[M].江苏:中国矿业大学出版社,2009.
[52] Xin Song, Cheng Weimin, Wang Chunyuan.Marginal Analysis Technology of Best Safety Investment[C] . Proceedings of 2006(Shenyang)International Colloquium on Safety Science and Technology.Shenyang:Liaoning Science and Technology Publishing House, 2006(26): 52-56.
[53]侯江峰.矿井透水事故预防探讨[J].山西煤炭,2010,30(3):65-67.
[54]邢冬梅,叶义成,赵雯雯.我国矿山透水事故的统计分析及安全管理对策[J].金属矿山,2010(6):76-78.
[55]黄景坡,王为标.浅析贾汪矿区降雨量与涌水量的关系[J].煤炭科技,2006,2(1):9-11.
[56]隋鹏程,陈宝智,隋旭.安全原理[M].北京:化学工业出版社,2005.