基于FP-growth算法的高寒草地退化因素关联度分析
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摘要
为探讨多因素组合作用下青海省高寒草地退化的情况,量化并挖掘多因素集与青海省草地退化之间的关联关系,文中以高寒草地为研究对象,通过收集2005—2014年青海省气象数据、社会经济等各方面的数据并进行分级,以草地NDVI指数为退化评价指标,采用FP-growth算法来分析高寒草地植被覆盖退化与影响因子之间的关联性,经过置信度筛选得到以下结果:影响青海省高寒草地退化的影响因素集为[G4,Q1,J2],即当GDP均值在较高水平(157.8亿元～191.76亿元)、气温均值在较低水平(-15.77～-8.94℃)以及降水均值在较低水平(385.18～1 570.80 mm)的同时发生条件下,草地盖度较低,草地容易产生退化的情况。该结果具有较高的置信度,验证了FP-growth算法在草地退化多因素组合研究中的可行性。
In order to explore the degradation of alpine grassland in Qinghai province caused by the combination of various influencing factors,the relationship between multi-factor set and grassland degradation in Qinghai province was quantified and explored.In this paper,alpine grassland is taken as the research object.The data of various aspects such as meteorological data and social economy in Qinghai province from 2005 to 2014 are collected and graded.The grassland NDVI index is used as the degradation evaluation index,and the FP-growth algorithm is used to analyze the correlation between vegetation cover degradation and impact factors of alpine grassland.Through the higher confidence screening,the following conclusions: the influencing factors affecting the degradation of alpine grassland in Qinghai province are [G4,Q1,J2],that is,when the GDP average is at a higher level(157.8 billion yuan～ 19.17 billion yuan),when the average temperature is at a low level(-15.77～-8.94 ℃) and the average precipitation value is at a low level(385.18～1 570.80 mm),the grass cover is low and the grass is prone to degradation.The results have high confidence,which verifies the feasibility of the FP-growth algorithm in the multi-factor combination of grassland degradation.
In order to explore the degradation of alpine grassland in Qinghai province caused by the combination of various influencing factors,the relationship between multi-factor set and grassland degradation in Qinghai province was quantified and explored.In this paper,alpine grassland is taken as the research object.The data of various aspects such as meteorological data and social economy in Qinghai province from 2005 to 2014 are collected and graded.The grassland NDVI index is used as the degradation evaluation index,and the FP-growth algorithm is used to analyze the correlation between vegetation cover degradation and impact factors of alpine grassland.Through the higher confidence screening,the following conclusions: the influencing factors affecting the degradation of alpine grassland in Qinghai province are [G4,Q1,J2],that is,when the GDP average is at a higher level(157.8 billion yuan～ 19.17 billion yuan),when the average temperature is at a low level(-15.77～-8.94 ℃) and the average precipitation value is at a low level(385.18～1 570.80 mm),the grass cover is low and the grass is prone to degradation.The results have high confidence,which verifies the feasibility of the FP-growth algorithm in the multi-factor combination of grassland degradation.
引文
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[18] 吴培中,陈俊明,陈松林.空间关联规则在土地利用与地形特征关系研究中的应用[J].亚热带资源与环境学报,2011,6(4):64-69.
[19] 姚幸,韩建民.西北牧区经济发展与草地退化的关系[J].草业科学,2015,32(4):628-634.
[20] 旦正巷前.班玛县气温和降雨量对高寒草甸杂类草产草量的影响[J].安徽农业科学,2015,43(30):187-189.
[21] 龙瑞军,董世魁,胡自治.西部草地退化的原因分析与生态恢复措施探讨[J].草原与草坪,2005(6):3-7.
[2] 卢满意.锡林郭勒草原退化影响因素分析及可持续利用对策研究[D].呼和浩特:内蒙古农业大学,2012.
[3] 卢虎,姚拓,李建宏,等.高寒地区不同退化草地植被和土壤微生物特性及其相关性研究[J].草业学报,2015,24(5):34-43.
[4] 蒋志清,朱美玲,宋玉兰,等.新疆草地退化影响因素分析——基于聚类的分析[J].经济研究导刊,2012(16):51-54.
[5] 李传新.基于遥感的锡林河流域草地退化及影响因素分析[D].北京:中国地质大学(北京),2016.
[6] 张军,张彩琴,李茜若,等.内蒙古典型草原牧草生物量与气象因子的灰色关联优势分析[J].干旱区资源与环境,2016,30(4):138-143.
[7] 闫颖慧,胡小霞.基于灰色关联法的甘南草地退化影响因素分析——以碌曲县为例[J].生态经济,2010(2):135-137.
[8] 自然资源部.土地利用现状分类:GB/T 21010—2007[S].北京:中国标准出版社,2007.
[9] 周伟,王倩,章超斌,等.黑河中上游草地NDVI时空变化规律及其对气候因子的响应分析[J].草业学报,2013,22(1):138-147.
[10] 徐新良.中国月度植被指数(NDVI)空间分布数据集[EB/OL].(2018-06-06)[2018-09-12].http://www.resdc.cn/DOI,2018.DOI10.12078/2018060602.
[11] 杨云,罗艳霞.FP-growth算法的改进[J].计算机工程与设计,2010,31(7):1506-1509.
[12] 杨勇,王伟.一种基于MapReduce的并行FP-growth算法[J].重庆邮电大学学报(自然科学版),2013,25(5):651-657.
[13] LI H,WANGY,ZHANG D,et al.PFP:parallel FP-growth for query recommendation[J].ACM Recommender Systems,2006(3):43-51.
[14] 章志刚,吉根林.一种基于FP-growth的频繁项目集并行挖掘算法[J].计算机工程与应用,2014,50(2):103-106.
[15] 颜跃进,李舟军,陈火旺.基于FP-Tree有效挖掘最大频繁项集[J].软件学报,2005(2):215-222.
[16] HAN J,PEI J,YIN Y,et al.Mining Frequent Patterns without Candidate Generation:A Frequent-Pattern Tree Approach[J].Data Mining and Knowledge Discovery,2004,8(1):53-87.
[17] 郝璐,高景民,杨春燕.内蒙古天然草地退化成因的多因素灰色关联分析[J].草业学报,2006,15(6):26-31.
[18] 吴培中,陈俊明,陈松林.空间关联规则在土地利用与地形特征关系研究中的应用[J].亚热带资源与环境学报,2011,6(4):64-69.
[19] 姚幸,韩建民.西北牧区经济发展与草地退化的关系[J].草业科学,2015,32(4):628-634.
[20] 旦正巷前.班玛县气温和降雨量对高寒草甸杂类草产草量的影响[J].安徽农业科学,2015,43(30):187-189.
[21] 龙瑞军,董世魁,胡自治.西部草地退化的原因分析与生态恢复措施探讨[J].草原与草坪,2005(6):3-7.