基于灰色Markov-GMP-Verhulst模型的黄河宁蒙段冰凌灾害风险预测
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摘要
为了提高冰凌灾害的风险预测的可靠性,针对冰凌灾害风险的动态非线性特征,构建能够识别风险波动变化规律的灰色GMP(1,1,N)-Verhulst组合预测模型,同时引入信息熵理论的知识,提出基于Markov链修正的熵权法灰色组合预测方法。以黄河宁蒙段2005～2014年冰凌灾害风险值作为原始数据序列进行模型拟合,并对2015～2017年的冰凌灾害风险进行预测。计算得出在已知实际冰凌风险值的年份内,灰色Markov-GMP-Verhulst模型的预测精度比单一灰色预测模型更加精确,结合实际情况评估2015～2016、2016～2017年的冰凌灾害风险值,并与Markov链修正的组合模型的预测值进行对比分析,预测结果与实际值的吻合性良好,进一步验证了模型的合理可操作性,以期为黄河宁蒙河段的凌汛灾害防治提供借鉴。
In order to improve the reliability of icicle hazard risk prediction aiming at the dynamic nonlinear characteristics of ice-jam disaster risk, a grey GMP(1,1,N)-Verhulst combination forecasting model which can identify the change law of risk fluctuation is constructed. At the same time, the knowledge of information entropy theory is introduced, and the gray combination forecasting method based on Markov chain modification is proposed. The model was fitted with the icicle hazard risk value of the Yellow River Ningxia-Inner Mongolia reaches from 2005 to 2014 as the original data series, and the ice-jam disaster risk from 2015 to 2017 was predicted. The prediction accuracy of the gray Markov-GMP-Verhulst model is more accurate than the single gray prediction model in the year when the actual ice risk value is known. The ice-jam disaster risk values of 2015 to 2016 and 2016 to 2017 are evaluated according to the actual situation. Compared with the predicted values of the Markov chain modified combination model, the prediction results are in good agreement with the actual values, and the reasonable operability of the model is further verified, in order to provide reference for the prevention and control of the flood disaster in the Ningxia-Inner Mongolia Reaches of the Yellow River.
In order to improve the reliability of icicle hazard risk prediction aiming at the dynamic nonlinear characteristics of ice-jam disaster risk, a grey GMP(1,1,N)-Verhulst combination forecasting model which can identify the change law of risk fluctuation is constructed. At the same time, the knowledge of information entropy theory is introduced, and the gray combination forecasting method based on Markov chain modification is proposed. The model was fitted with the icicle hazard risk value of the Yellow River Ningxia-Inner Mongolia reaches from 2005 to 2014 as the original data series, and the ice-jam disaster risk from 2015 to 2017 was predicted. The prediction accuracy of the gray Markov-GMP-Verhulst model is more accurate than the single gray prediction model in the year when the actual ice risk value is known. The ice-jam disaster risk values of 2015 to 2016 and 2016 to 2017 are evaluated according to the actual situation. Compared with the predicted values of the Markov chain modified combination model, the prediction results are in good agreement with the actual values, and the reasonable operability of the model is further verified, in order to provide reference for the prevention and control of the flood disaster in the Ningxia-Inner Mongolia Reaches of the Yellow River.
引文
[1] 马喜祥,白世录,袁学安,等.中国河流冰情[M].郑州:黄河水利出版社.2009.MA Xixiang,BAI Shilu,YUAN Xuean,et al.The Chinese River Ice Situation [M].Zhengzhou:Yellow River Water Conservancy Press,2009.(in Chinese)
[2] Luo D.Risk evaluation of ice-jam disasters using gray systems theory:The case of Ningxia-Inner Mongolia reaches of the Yellow River[J].Natural Hazards,2014,71(3):1419-1431.
[3] Dang Luo,Wenxin Mao,Huifang.Risk assessment and analysis of ice disaster in Ning-Meng reach of Yellow River based on a two-phased intelligent model under grey information environment[J].Natural Hazards,2017,88(1):591–610.
[4] WU C G,WEI Y M,JIN JL,et al.Comprehensive evaluation of ice disaster risk of the Ningxia-Inner Mongolia Reach in the upper Yellow River [J].Natural Hazards,2015,75(2):179-197.
[5] 罗党,韦保磊.灰色GMP(1,1,N)模型及其在冰凌灾害风险预测中的应用[J].系统工程理论与实践,2017,37(11):2929-2937.LUO Dang,WEI Baolei.Grey GMP(1,1,N) model and its application in risk prediction of ice-jam disaster[J].Systems Engineering-Theory & Practice,2017,37(11):2929-2937.(in Chinese)
[6] 吴佳林.基于灰信息的黄河冰凌灾害风险评估研究[D].华北水利水电大学,2017.WU Jialin.RiskAssessment of Ice Disaster in the Yellow River Based on Grey Information Decision Method[D].North China University of Water Resources and Electric Power,2017.(in Chinese)
[7] 罗党,刘敏.基于灰信息的黄河冰凌灾害风险评估模型[J].华北水利水电大学学报(自然科学版),2016,37(06):72-77,92.LUO Dang,LIU Min.Risk assessment model of ice disaster in the Yellow River based on grey information[J].Journal of North China University of Water Resources and Electric Power (Natural Science Edition),2016,37(06):72-77,92.(in Chinese)
[8] 张峰.伊犁河水动力因素与冰凌灾害关系[J].东北水利水电,2011,29(10):27-28.ZHANG Feng.Relationship between hydrodynamic factors and ice disasters in Yili River[J].Water Resources & Hydropower of Northeast China,2011,29(10):27-28.
[9] Jun WANG,Fa-yi SHI,Pang-pang CHEN,et al.Simulations of ice jam thickness distribution in the transverse direction[J].Journal of Hydrodynamics,Ser.B,2014,26(5):762-769.
[10] 赵永峰,郑慧.大幅降温对纳尔松河冰凌形成影响的模拟分析[J].科技通报,2016,32(11):51-55.ZHAO Yongfeng,ZHENG Hui.Simulation analysis of substantial cooling effect to Narson River Icicle formation[J].Bulletin of Science and Technology,2016,32(11):51-55.
[11] 鲁仕宝,黄强,吴成国,等.黄河宁蒙段冰凌灾害及水库防凌措施[J].自然灾害学报,2010,19(4):43-47.LU Shibao,HUANG Qiang,WU Chengguo,et al.Ice jams disaster in Ningxia-Inner Mongolia reaches of the Yellow-River and its prevention by reservoirs[J].Journal of Natural Disasters,2010,19(4):43-47.
[12] 那济海,周秀杰,许秀红,等.黑龙江、松花江和嫩江冰坝凌汛发生原因及预报方法[J].自然灾害学报,2011,20(2):115-120.NA Jihai,ZHOU Xiujie,XU Xiuhong,et al.Cause and forecast of ice jam and run in Heilong River,Songhua River and Nenjiang River[J].Journal of Natural Disasters,2011,20(2):115-120.
[13] 贺政纲,黄娟.基于FPSO灰色Verhulst模型的铁路货运量预测[J].铁道学报,2018,40(08):1-8.HE Zhenggang,HUANG Juan.Prediction of railway freight volumes based on FPSO grey Verhulst model[J].Journal of China Railway Society,2018,40(08):1-8.
[14] 周艳萍.基于灰色Verhulst模型的山西太原地面沉降趋势分析[J].中国地质灾害与防治学报,2018,29(02):94-99.ZHOU Yanping.Land subsidence trend of Taiyuan City,Shanxi based on grey Verhust model[J].The Chinese Journal of Geological Hazard and Control,2018,29(02):94-99.
[15] 马涛.组合预测方法及其应用研究[D].兰州:兰州大学,2017.MA Tao.A Research on Combining Forecasting Methods and Its Applications[D].Lanzhou:Lanzhou University,2017.
[16] 贾鼎元,柴乃杰,王恩茂.基于Markov链修正的铁路运量灰色组合预测模型研究[J/OL].铁道标准设计,2018,63(2):1-6.JIA Dingyuan,CHAI Naijie,WANG Enmao.Research on modified grey combination forecasting model of railway transportation volume based on Markov chain[J/OL].Railway Standard Design,2018,63 (2):1-6.
[17] 陈云翔,董晓雄,项华春,等.基于信息熵的群组聚类组合赋权法[J].中国管理科学,2015,23 (6):142-146.CHEN Yunxiang,DONG Xiaoxiong,XIANG Huachun,et al.Method for combination weighting experts based on information entropy and cluster analysis[J].Chinese Journal of Management Science,2015,23 (6):142-146.
[18] 靳春玲,王运鑫.基于灰色马尔科夫模型的突发水污染事故预测[J].兰州交通大学学报,2018,37(2):110-115.JIN Chunling,WANG Yunxin.The prediction of sudden water pollution accident based on gray Markov model[J].Journal of Lanzhou Jiaotong University,2018,37(2):110-115.
[19] 霍世青,温立叶,范昊昊,等.2014-2015年度黄河宁蒙河段凌情及气象成因[J].人民黄河,2016,38(02):16-18,23.HUO Shiqing,WEN Liye,FAN Haohao.Analysis of ice flood characteristics and at Ningxia-Inner Mongolia reach of Yellow Meteorological Causesver in year 2014-2015[J].Yellow River,2016,38(02):16-18,23.
[20] 刘晓岩.2015-2016年度黄河防凌形势及防御措施[J].中国防汛抗旱,2015,25(6):1-5.LIU Xiaoyan.Yellow River anti-ice disaster situation and defense measures from 2015 to 2016[J].China Flood,2015,25(6):1-5.
[21] 梁贵生,梁恒.2016-2017年度黄河宁蒙河段凌情特性分析[J].中国防汛抗旱,2018,28(4):63-66LIANG Guisheng,LIANG Heng.Analysis of the characteristics of ice-jam disasters in Ningxia-Inner Mongolia reaches of the Yellow River in 2016-2017[J].China Flood,2018,28(4):63-66.
[2] Luo D.Risk evaluation of ice-jam disasters using gray systems theory:The case of Ningxia-Inner Mongolia reaches of the Yellow River[J].Natural Hazards,2014,71(3):1419-1431.
[3] Dang Luo,Wenxin Mao,Huifang.Risk assessment and analysis of ice disaster in Ning-Meng reach of Yellow River based on a two-phased intelligent model under grey information environment[J].Natural Hazards,2017,88(1):591–610.
[4] WU C G,WEI Y M,JIN JL,et al.Comprehensive evaluation of ice disaster risk of the Ningxia-Inner Mongolia Reach in the upper Yellow River [J].Natural Hazards,2015,75(2):179-197.
[5] 罗党,韦保磊.灰色GMP(1,1,N)模型及其在冰凌灾害风险预测中的应用[J].系统工程理论与实践,2017,37(11):2929-2937.LUO Dang,WEI Baolei.Grey GMP(1,1,N) model and its application in risk prediction of ice-jam disaster[J].Systems Engineering-Theory & Practice,2017,37(11):2929-2937.(in Chinese)
[6] 吴佳林.基于灰信息的黄河冰凌灾害风险评估研究[D].华北水利水电大学,2017.WU Jialin.RiskAssessment of Ice Disaster in the Yellow River Based on Grey Information Decision Method[D].North China University of Water Resources and Electric Power,2017.(in Chinese)
[7] 罗党,刘敏.基于灰信息的黄河冰凌灾害风险评估模型[J].华北水利水电大学学报(自然科学版),2016,37(06):72-77,92.LUO Dang,LIU Min.Risk assessment model of ice disaster in the Yellow River based on grey information[J].Journal of North China University of Water Resources and Electric Power (Natural Science Edition),2016,37(06):72-77,92.(in Chinese)
[8] 张峰.伊犁河水动力因素与冰凌灾害关系[J].东北水利水电,2011,29(10):27-28.ZHANG Feng.Relationship between hydrodynamic factors and ice disasters in Yili River[J].Water Resources & Hydropower of Northeast China,2011,29(10):27-28.
[9] Jun WANG,Fa-yi SHI,Pang-pang CHEN,et al.Simulations of ice jam thickness distribution in the transverse direction[J].Journal of Hydrodynamics,Ser.B,2014,26(5):762-769.
[10] 赵永峰,郑慧.大幅降温对纳尔松河冰凌形成影响的模拟分析[J].科技通报,2016,32(11):51-55.ZHAO Yongfeng,ZHENG Hui.Simulation analysis of substantial cooling effect to Narson River Icicle formation[J].Bulletin of Science and Technology,2016,32(11):51-55.
[11] 鲁仕宝,黄强,吴成国,等.黄河宁蒙段冰凌灾害及水库防凌措施[J].自然灾害学报,2010,19(4):43-47.LU Shibao,HUANG Qiang,WU Chengguo,et al.Ice jams disaster in Ningxia-Inner Mongolia reaches of the Yellow-River and its prevention by reservoirs[J].Journal of Natural Disasters,2010,19(4):43-47.
[12] 那济海,周秀杰,许秀红,等.黑龙江、松花江和嫩江冰坝凌汛发生原因及预报方法[J].自然灾害学报,2011,20(2):115-120.NA Jihai,ZHOU Xiujie,XU Xiuhong,et al.Cause and forecast of ice jam and run in Heilong River,Songhua River and Nenjiang River[J].Journal of Natural Disasters,2011,20(2):115-120.
[13] 贺政纲,黄娟.基于FPSO灰色Verhulst模型的铁路货运量预测[J].铁道学报,2018,40(08):1-8.HE Zhenggang,HUANG Juan.Prediction of railway freight volumes based on FPSO grey Verhulst model[J].Journal of China Railway Society,2018,40(08):1-8.
[14] 周艳萍.基于灰色Verhulst模型的山西太原地面沉降趋势分析[J].中国地质灾害与防治学报,2018,29(02):94-99.ZHOU Yanping.Land subsidence trend of Taiyuan City,Shanxi based on grey Verhust model[J].The Chinese Journal of Geological Hazard and Control,2018,29(02):94-99.
[15] 马涛.组合预测方法及其应用研究[D].兰州:兰州大学,2017.MA Tao.A Research on Combining Forecasting Methods and Its Applications[D].Lanzhou:Lanzhou University,2017.
[16] 贾鼎元,柴乃杰,王恩茂.基于Markov链修正的铁路运量灰色组合预测模型研究[J/OL].铁道标准设计,2018,63(2):1-6.JIA Dingyuan,CHAI Naijie,WANG Enmao.Research on modified grey combination forecasting model of railway transportation volume based on Markov chain[J/OL].Railway Standard Design,2018,63 (2):1-6.
[17] 陈云翔,董晓雄,项华春,等.基于信息熵的群组聚类组合赋权法[J].中国管理科学,2015,23 (6):142-146.CHEN Yunxiang,DONG Xiaoxiong,XIANG Huachun,et al.Method for combination weighting experts based on information entropy and cluster analysis[J].Chinese Journal of Management Science,2015,23 (6):142-146.
[18] 靳春玲,王运鑫.基于灰色马尔科夫模型的突发水污染事故预测[J].兰州交通大学学报,2018,37(2):110-115.JIN Chunling,WANG Yunxin.The prediction of sudden water pollution accident based on gray Markov model[J].Journal of Lanzhou Jiaotong University,2018,37(2):110-115.
[19] 霍世青,温立叶,范昊昊,等.2014-2015年度黄河宁蒙河段凌情及气象成因[J].人民黄河,2016,38(02):16-18,23.HUO Shiqing,WEN Liye,FAN Haohao.Analysis of ice flood characteristics and at Ningxia-Inner Mongolia reach of Yellow Meteorological Causesver in year 2014-2015[J].Yellow River,2016,38(02):16-18,23.
[20] 刘晓岩.2015-2016年度黄河防凌形势及防御措施[J].中国防汛抗旱,2015,25(6):1-5.LIU Xiaoyan.Yellow River anti-ice disaster situation and defense measures from 2015 to 2016[J].China Flood,2015,25(6):1-5.
[21] 梁贵生,梁恒.2016-2017年度黄河宁蒙河段凌情特性分析[J].中国防汛抗旱,2018,28(4):63-66LIANG Guisheng,LIANG Heng.Analysis of the characteristics of ice-jam disasters in Ningxia-Inner Mongolia reaches of the Yellow River in 2016-2017[J].China Flood,2018,28(4):63-66.
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