湖北省雷击关联规则挖掘发现
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摘要
利用湖北省雷电实时监测系统中2012~2014年间7月份的历史数据,挖掘雷击与地形、气象等环境要素之间的关联.结合具有噪声的基于密度的聚类算法与核密度算法,分层次筛选候选集,剔除低密度的无兴趣噪声点.利用先验关联规则挖掘算法进行差异化挖掘,得到湖北省不同等级电流幅值的雷击点与地形及气象因素之间的定性定量关系.分析表明,在地势低矮的湖北省,10~75 k A的雷击常发生于土地覆盖类型为林地和农田的区域,主要与日均气压、气温、相对湿度、坡度、水汽压、风速、最大风速、降水量有强相关性.100~157k A的中强电流幅值雷击与最大风速、日均水汽压、气温、最低气温、均风速的相关性较强.157.9~250.7 k A的强电流幅值雷击主要与降水量相关.
Based on the historical data of each July between 2012 and 2014 provided by the lightning location system of Hubei province,this paper reveals possible relationship between lightning and various environmental factors concerning meteorological conditions and terrains.Combined with the algorithm of density-based spatial clustering of applications with noise(DBSCAN) and the kernel density algorithm,insignificant locations with rare lightning are eliminated.Qualitative and quantitative relations between lightning locations in Hubei with different peak currents and various factors such as terrains and meteorological factors are obtained using the a priori data mining algorithm for correlation rules.In Hubei,which is mainly a low-lying land,lightning strikes with low current of l0~75 k A are likely to occur in forests or farmlands.In these areas,lightning is strongly correlated with average values of air pressure,relative humidity,vapor pressure,precipitation,temperature and wind speed.Lightning with high current of more than 100 k A has a certain relation with wind speed,daily vapor pressure,daily average temperature and minimum temperature.Lightning with very high current of 157.9~250.7 k A is correlated with precipitation.
Based on the historical data of each July between 2012 and 2014 provided by the lightning location system of Hubei province,this paper reveals possible relationship between lightning and various environmental factors concerning meteorological conditions and terrains.Combined with the algorithm of density-based spatial clustering of applications with noise(DBSCAN) and the kernel density algorithm,insignificant locations with rare lightning are eliminated.Qualitative and quantitative relations between lightning locations in Hubei with different peak currents and various factors such as terrains and meteorological factors are obtained using the a priori data mining algorithm for correlation rules.In Hubei,which is mainly a low-lying land,lightning strikes with low current of l0~75 k A are likely to occur in forests or farmlands.In these areas,lightning is strongly correlated with average values of air pressure,relative humidity,vapor pressure,precipitation,temperature and wind speed.Lightning with high current of more than 100 k A has a certain relation with wind speed,daily vapor pressure,daily average temperature and minimum temperature.Lightning with very high current of 157.9~250.7 k A is correlated with precipitation.
引文
[1]Proestakis E,Kazadzis S,Lagouvardos K,Kotroni V,Kazantzidis A.Lightning activity and aerosols in the Mediterranean region[J].Atmospheric Research,2016,170:66-75.
[2]赵芝,石季英,袁启海,林济铿,叶剑华,胡世骏.输电线路的雷击跳闸概率预测计算新方法[J].电力系统自动化,2015,39(3):51-58.Zhao Z,Shi J Y,Yuan Q H,Lin J K,Ye J H,Hu S J.A new prediction calculation method of transmission lines lightning trip probability[J].Automation of Electric Power Systems,2015,39(3):51-58.(in Chinese)
[3]赵可新,宋庆武,乌秋力,黄文震,姜凤友.原始林区雷击火发生的气象条件及预警技术[J].内蒙古气象,2006(2):22-24.Zhao K X,Song Q W,Wu Q L,Huang W Z,Jiang F Y.Original forests lightning fire occurrence of meteorological conditions and early warning technology[J].Meteorology Journal of Inner Mongolia,2006(2):22-24.(in Chinese)
[4]郭福涛,胡海清,金森,马至海,张扬.基于负二项和零膨胀负二项回归模型的大兴安岭地区雷击火与气象因素的关系[J].植物生态学报,2010,34(5):571-577.Guo F T,Hu H Q,Jin S,Ma Z H,Zhang Y.Relationship between forest lighting fire occurrence and weather factors in Daxing’an Mountains based on negative binomial model and zero-inflated negative binomial models[J].Chinese Journal of Plant Ecology,2010,34(5):571-577.(in Chinese)
[5]俞立婷.输电线路走廊雷电观测数据的挖掘整理[D].武汉:华中科技大学,2007.
[6]赵伟,童杭伟,张俊,浦静姣.浙江省雷电时空分布特征及影响因素分析[J].电网技术,2013,37(5):1425-1431.Zhao W,Tong H W,Zhang J,Pu J J.Temporal-spatial distribution characteristics of thunder and lightning in Zhejiang province and influencing factors[J].Power System Technology,2013,37(5):1425-1431.(in Chinese)
[7]Mazarakis N,Kotroni V,Lagouvardos K,Argiriou A A.Storms and lightning activity in greece during the warm periods of 2003-2006[J].Journal of Applied Meteorology&Climatology,2008,47(12):3089-3098.
[8]Prentice S A,Mackerras D.The ratio of cloud to cloud-ground lightning flashes in thunderstorms[J].Journal of Applied Meteorology,1977,16(5):545-550.
[9]Zheng D,Zhang Y,Meng Q,Chen L W,Dan J R.Climatology of lightning activity in South China and its relationships to precipitation and convective available potential energy[J].Advances in Atmospheric Sciences,2016,33(3):365-376.
[10]Proestakis E,Kazadzis S,Lagouvardos K,Kotroni V,Kazantzidis A.Lightning activity and aerosols in the Mediterranean region[J].Atmospheric Research,2016,170:66-75.
[11]陈勇伟,郑涛,王汉堃,王琦,梁耀丹.基于BP神经网络模型的雷电潜势预报[J].干旱气象,2013,31(3):595-601.Chen Y W,Zheng T,Wang H K,Wang Q,Liang Y D.Thunderstorm potential prediction based on back propagation neural network[J].Journal of Arid Meteorology,2013,31(3):595-601.(in Chinese)
[12]俞立婷,何俊佳,陈家宏.输电线路雷电活动时空分布特征的数据挖掘[J].高电压技术,2008,2:314-318.Yu L T,He J J,Chen J H.Data analysis of the time and space distribution of lightning[J].High Voltage Engineering,2008,2:314-318.(in Chinese)
[13]赵超,舒红,朱欣焰,戴上平.气象数据概化中的最佳聚类数研究[J].华中师范大学学报(自然科学版),2008(3):490-493.Zhao C,Shu H,Zhu X Y,Dai S P.Determination of the optimal class number in meteorological data generalization[J].Journal of Huazhong Normal University(Natural Science),2008(3):490-493.(in Chinese)
[14]王学良,刘学春,黄小彦,史雅静.湖北地区云地闪电时空分布特征分析[J].气象,2010(10):91-96.Wang X L,Liu X C,Huang X Y,Shi Y J.Analysis of the spatial and temporal distribution characteristics of the cloud-ground lightning in Hubei area[J].Meteorological Monthly,2010(10):91-96.(in Chinese)
[15]赵海波.基于GIS的城市气象监测要素插值分析研究[D].昆明理工大学,2010.
[16]Han J,Kamber M,Pei J.Data mining:concepts and techniques:concepts and techniques[M].[s.l.]:Elsevier Science,2011.
[2]赵芝,石季英,袁启海,林济铿,叶剑华,胡世骏.输电线路的雷击跳闸概率预测计算新方法[J].电力系统自动化,2015,39(3):51-58.Zhao Z,Shi J Y,Yuan Q H,Lin J K,Ye J H,Hu S J.A new prediction calculation method of transmission lines lightning trip probability[J].Automation of Electric Power Systems,2015,39(3):51-58.(in Chinese)
[3]赵可新,宋庆武,乌秋力,黄文震,姜凤友.原始林区雷击火发生的气象条件及预警技术[J].内蒙古气象,2006(2):22-24.Zhao K X,Song Q W,Wu Q L,Huang W Z,Jiang F Y.Original forests lightning fire occurrence of meteorological conditions and early warning technology[J].Meteorology Journal of Inner Mongolia,2006(2):22-24.(in Chinese)
[4]郭福涛,胡海清,金森,马至海,张扬.基于负二项和零膨胀负二项回归模型的大兴安岭地区雷击火与气象因素的关系[J].植物生态学报,2010,34(5):571-577.Guo F T,Hu H Q,Jin S,Ma Z H,Zhang Y.Relationship between forest lighting fire occurrence and weather factors in Daxing’an Mountains based on negative binomial model and zero-inflated negative binomial models[J].Chinese Journal of Plant Ecology,2010,34(5):571-577.(in Chinese)
[5]俞立婷.输电线路走廊雷电观测数据的挖掘整理[D].武汉:华中科技大学,2007.
[6]赵伟,童杭伟,张俊,浦静姣.浙江省雷电时空分布特征及影响因素分析[J].电网技术,2013,37(5):1425-1431.Zhao W,Tong H W,Zhang J,Pu J J.Temporal-spatial distribution characteristics of thunder and lightning in Zhejiang province and influencing factors[J].Power System Technology,2013,37(5):1425-1431.(in Chinese)
[7]Mazarakis N,Kotroni V,Lagouvardos K,Argiriou A A.Storms and lightning activity in greece during the warm periods of 2003-2006[J].Journal of Applied Meteorology&Climatology,2008,47(12):3089-3098.
[8]Prentice S A,Mackerras D.The ratio of cloud to cloud-ground lightning flashes in thunderstorms[J].Journal of Applied Meteorology,1977,16(5):545-550.
[9]Zheng D,Zhang Y,Meng Q,Chen L W,Dan J R.Climatology of lightning activity in South China and its relationships to precipitation and convective available potential energy[J].Advances in Atmospheric Sciences,2016,33(3):365-376.
[10]Proestakis E,Kazadzis S,Lagouvardos K,Kotroni V,Kazantzidis A.Lightning activity and aerosols in the Mediterranean region[J].Atmospheric Research,2016,170:66-75.
[11]陈勇伟,郑涛,王汉堃,王琦,梁耀丹.基于BP神经网络模型的雷电潜势预报[J].干旱气象,2013,31(3):595-601.Chen Y W,Zheng T,Wang H K,Wang Q,Liang Y D.Thunderstorm potential prediction based on back propagation neural network[J].Journal of Arid Meteorology,2013,31(3):595-601.(in Chinese)
[12]俞立婷,何俊佳,陈家宏.输电线路雷电活动时空分布特征的数据挖掘[J].高电压技术,2008,2:314-318.Yu L T,He J J,Chen J H.Data analysis of the time and space distribution of lightning[J].High Voltage Engineering,2008,2:314-318.(in Chinese)
[13]赵超,舒红,朱欣焰,戴上平.气象数据概化中的最佳聚类数研究[J].华中师范大学学报(自然科学版),2008(3):490-493.Zhao C,Shu H,Zhu X Y,Dai S P.Determination of the optimal class number in meteorological data generalization[J].Journal of Huazhong Normal University(Natural Science),2008(3):490-493.(in Chinese)
[14]王学良,刘学春,黄小彦,史雅静.湖北地区云地闪电时空分布特征分析[J].气象,2010(10):91-96.Wang X L,Liu X C,Huang X Y,Shi Y J.Analysis of the spatial and temporal distribution characteristics of the cloud-ground lightning in Hubei area[J].Meteorological Monthly,2010(10):91-96.(in Chinese)
[15]赵海波.基于GIS的城市气象监测要素插值分析研究[D].昆明理工大学,2010.
[16]Han J,Kamber M,Pei J.Data mining:concepts and techniques:concepts and techniques[M].[s.l.]:Elsevier Science,2011.
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