基于改进TOPSIS模型的黑龙江省西部半干旱地区农业旱灾脆弱性评价
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摘要
采用灰色关联度分析确定指标权重,改进TOPSIS模型,根据各评价单位无量纲数据列标准差确定灰色关联度分析中分辨系数,不计主观赋值随意性和固定性情况下,农业旱灾脆弱性评价结果客观合理。将改进模型应用于黑龙江省西部半干旱地区2009~2014年农业旱灾脆弱性评价,深度分析区域农业旱灾脆弱性时空异变,了解区域农业旱灾脆弱性影响因素。结果表明,基于改进灰色关联度分析确定指标权重值为动态值,突变数据对指标权重排序影响更具时效性;黑龙江省西部半干旱地区农业旱灾脆弱性多为重度脆弱和中度脆弱,属于极易发生旱灾地区;近年来,该区域实现粮食增产同时,合理调控区域复合体影响因素,大部分农业地区农业旱灾脆弱性强度减弱,少部分地区保持相对稳定。评价结果与实际遭受旱灾损失性质基本吻合,评价模型具有可行性和有效性,可为农业旱灾脆弱性评价提供参考。
Through the analysis of the deficiency of TOPSIS model, the grey incidence degree analysis to determine the index weight was be used, and then determined the resolution ratio in grey incidence degree analysis, according to the standard deviation of the dimensionless data series of each evaluation unit, excluded the subjective assignment randomness and fixity so as to make the relative similarity degree of the assessment of agricultural drought vulnerability of model calculation objective and reasonable. The improved model was applied to assess the agricultural drought vulnerability in semi-arid region of western Heilongjiang Province in 2009-2014. The spatial-temporal variation of regional agricultural drought vulnerability was analyzed in depth to understanding the influence factors of vulnerability of regional agricultural drought. The results showed that the index weights based on improved grey incidence degree analysis were dynamic values, excluding the randomness and fixity of the subjective valuation method, and synchronizing with the data, paying more attentions to the influence of the mutation data on the ranking of the index weights, which was more timeliness; in recent years, while policymakers in this region were committed to increase grain output, they controlled various aspects of the complexities of agricultural production in the region comprehensively and rationally, which caused there ducing intensity of agricultural drought vulnerability in most areas and steadily increasing grain output in a few areas. The results of the evaluation were in good agreement with the nature of drought losses actually suffered. The evaluation model was feasible and effective and could be used as a reference for the evaluation of agricultural drought vulnerability.
Through the analysis of the deficiency of TOPSIS model, the grey incidence degree analysis to determine the index weight was be used, and then determined the resolution ratio in grey incidence degree analysis, according to the standard deviation of the dimensionless data series of each evaluation unit, excluded the subjective assignment randomness and fixity so as to make the relative similarity degree of the assessment of agricultural drought vulnerability of model calculation objective and reasonable. The improved model was applied to assess the agricultural drought vulnerability in semi-arid region of western Heilongjiang Province in 2009-2014. The spatial-temporal variation of regional agricultural drought vulnerability was analyzed in depth to understanding the influence factors of vulnerability of regional agricultural drought. The results showed that the index weights based on improved grey incidence degree analysis were dynamic values, excluding the randomness and fixity of the subjective valuation method, and synchronizing with the data, paying more attentions to the influence of the mutation data on the ranking of the index weights, which was more timeliness; in recent years, while policymakers in this region were committed to increase grain output, they controlled various aspects of the complexities of agricultural production in the region comprehensively and rationally, which caused there ducing intensity of agricultural drought vulnerability in most areas and steadily increasing grain output in a few areas. The results of the evaluation were in good agreement with the nature of drought losses actually suffered. The evaluation model was feasible and effective and could be used as a reference for the evaluation of agricultural drought vulnerability.
引文
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[18]付强.数据处理方法及其农业应用[M].北京:科学出版社,2006.
[19]赵越,张晓琴.基于谱效关系的灰色关联度分辨系数确定[J].太原师范学报:自然科学版,2013(4):1-3.
[2]Shahid S,Behrawan H.Drought risk assessment in the western part of Bangladess[J].Natural Hazards,2008,46(3):391-413.
[3]Zarafshani K,Sharafi L,Azadi H,et al.Drought vulnerability assessment:The case of wheat farmers in Western Iran[J].Global and Planetary Change,2012,98-99(6):122-130.
[4]Wilhelmi O V,Wilhite D A.Assessing Vulnerability to Agricultural Drought:A Nebraska Case Study[J].Natural Hazards,2002,25(1):37-58.
[5]Simelton E,Fraser E D G,Termansen M,et al.Typologies of crop-drought vulnerability:An empirical analysis of the socioeconomic factors that influence the sensitivity and resilience to drought of three major food crops in China(1961-2001)[J].Environmental Science and Policy,2009,12(4):438-452.
[6]Do N,Kang S.Assessing drought vulnerability using soil moisturebased water use efficiency measurements obtained from multisensor satellite data in Northeast Asia dryland regions[J].Journal of Arid Environments,2014,105:22-32.
[7]Safavi H R,Esfahani M K,Zamani A R.Integrated index for assessment of vulnerability to drought,case study:Zayandehrood River Basin,Iran[J].Water Resources Management,2014,28(6):1671-1688.
[8]杨彬云,吴容军,郑有飞,等.河北省农业旱灾脆弱性评价[J].安徽农业科学,2008,36(15):6499-6502.
[9]侯光良,肖景义,李生梅.基于气候变化的干旱脆弱性评价——以青海东部为例[J].自然灾害学报,2012,21(2):163-168.
[10]陈萍,陈晓玲.鄱阳湖生态经济区农业系统的干旱脆弱性评价[J].农业工程学报,2011,27(8):8-13.
[11]康永辉,解建仓,黄伟军,等.广西大石山区农业干旱成因分析及脆弱性评价[J].自然灾害学报,2014,23(3):24-32.
[12]王莺,王静,姚玉璧,等.基于主成分分析的中国南方干旱脆弱性评价[J].生态环境学报,2014,23(12):1897-1904.
[13]李梦娜,钱会,乔亮.关中地区农业干旱脆弱性评价[J].资源科学,2016,38(1):166-174.
[14]汪霞,汪磊.西南喀斯特地区农业旱灾脆弱性评价——以贵州省为例[J].广东农业科学,2014,41(22):181-186.
[15]曹永强,马静,李香云,等.投影寻踪技术在大连市农业干旱脆弱性评价中的应用[J].资源科学,2011,33(6):1106-1110.
[16]徐晗.基于熵权法的陕西省农业干旱脆弱性评价及影响因子识别[J].干旱地区农业研究,2016,34(3):198-205.
[17]梁祖美,曾金赐,杨振川.TOPSIS法在综合评价食品卫生工作中的应用[J].现代预防医学,2004,31(2):275-276.
[18]付强.数据处理方法及其农业应用[M].北京:科学出版社,2006.
[19]赵越,张晓琴.基于谱效关系的灰色关联度分辨系数确定[J].太原师范学报:自然科学版,2013(4):1-3.