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油茶象甲灾害预警指标体系研究
林海云,秦长生,赵丹阳,徐金柱,邱华龙,揭育泽
东莞市大岭山森林公园,广东省森林培育与保护利用重点实验室/广东省林业科学研究院,广东省森林培育与保护利用重点实验室/广东省林业科学研究院,广东省森林培育与保护利用重点实验室/广东省林业科学研究院,广东省森林培育与保护利用重点实验室/广东省林业科学研究院,广东省森林培育与保护利用重点实验室/广东省林业科学研究院
摘要:
将油茶象甲(Curculio chinensis)灾变过程中表现出的警源、警兆和警情相关的不同风险要素 作为统计指标,对预警指标体系指标值权重进行赋值并运算,构建了灾变监测预警模型,确定了油茶象 甲灾变预警等级。选取粤北地区 2015 年油茶象甲发生情况为例,进行风险警级评估,计算其警源、警兆 和警情值分别为 23.9、 33.5、 28.2,综合预警指标值为 Y=85.6,由此判定 2015 年油茶象甲在粤北地区预 警等级为重警,应发布红色预警信号。
关键词:  油茶象甲  灾害  预警  指标体系
DOI:
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基金项目:广东省林业科技创新专项项目“油茶象甲安全防控技术研究与示范”(2014KJCX020-03)。
Study on Early-warning Index System to Insect Attack of Curculio chinensis
LIN Haiyun,QIN Changsheng,ZHAO Danyang,XU Jinzhu,QIU Hualong and JIE Yuze
Dalingshan Forest Park,Dongguan,Guangdong Provincial Key Laboratory of Silviculture,Protection and Utilization,Guangdong Academy of Forestry,Guangzhou,Guangdong Provincial Key Laboratory of Silviculture,Protection and Utilization,Guangdong Academy of Forestry,Guangzhou,Guangdong Provincial Key Laboratory of Silviculture,Protection and Utilization,Guangdong Academy of Forestry,Guangzhou,Guangdong Provincial Key Laboratory of Silviculture,Protection and Utilization,Guangdong Academy of Forestry,Guangzhou,Guangdong Provincial Key Laboratory of Silviculture,Protection and Utilization,Guangdong Academy of Forestry,Guangzhou
Abstract:
The monitoring and early-warning models were structured by setting up the weight indexes of the early-warning index system, based on the different risky factors related to the risky source, risky sign and risky cause exhibiting in the process of calamities occurrence. This paper chose the case of Curculio chinensis in north Guangdong in 2015 as an example to carry on the risk warning level assessment, and the results showed that each factor index of source of warning, warning sign and warning cause was 23.9, 33.5 and 28.2, respectively, the integrated early-warning value was: Y = 85.6. The risky degree of calamity of C. chinensis was at the highest warning level, and red alerts should be delivered.
Key words:  Curculio chinensis  calamity  early-warning  index system