灰色模型的改进及其在气象干旱预测中的应用  

Improvement and application of grey model for meteorological drought prediction

作　　者：韩会明 刘喆玥 刘成林[1] 陈齐强 谢国栋 HAN Huiming;LIU Zheyue;LIU Chenglin;CHEN Qiqiang;XIE Guodong(School of Civil Engineering and Architecture,Nanchang University,Nanchang 330031,China;Jiangxi Meteorological Information Center,Nanchang 330096,China)

机构地区：[1]南昌大学建筑工程学院,南昌330031 [2]江西省气象信息中心,南昌330096

出　　处：《南水北调与水利科技》2019年第6期62-68,共7页South-to-North Water Transfers and Water Science & Technology

基　　金：江西省研究生创新专项资金项目(YC2018-S121)~~

摘　　要：针对传统GM(1,1)模型在数据预处理阶段存在的不足,分析模型误差的产生原因,提出新的数据预处理方法。依据峡江县1958-2018年年降水量资料,通过计算其SPI指数(标准化降水指数)得到干旱年时间序列。在真实的时间序列数据集上对经典灰色模型GM^0、平移转换预处理灰色模型GM^1、基于平移转换的平均弱化缓冲算子预处理灰色模型GM^2进行了对比测试,结果表明:平移转换结合平均弱化缓冲算子弥补了预处理阶段的不足,有效地降低传统模型的误差,改进后的GM^2模型的平均预测误差为3.32%,相较于其它两种模型分别降低了44.16%和16.24%。证明了该模型具有更好的预测精度,可应用于干旱年的预测,为区域干旱预测和干旱防治工作提供理论依据。In view of the shortcomings of the traditional Grey Model(1,1)(GM)(1,1)in the data preprocessing stage,a new data preprocessing method was proposed by analyzing the causes of model errors.Based on the annual precipitation data of Xiajiang County from 1958 to 2018,drought year time series was obtained by calculating SPI index(standardized precipitation index).The classical gray model GM^0,the translational conversion preprocessing gray model GM^1,and the average weakening buffer operator preprocessing gray model GM^2 based on translation transformation were compared on the real-time series data set.The results showed that the translational conversion combined with the average weakening buffer operator compensated the shortcomings of the preprocessing stage and effectively reduced the error of the traditional model.The average prediction error of the improved GM^2 model was 3.32%,which was 44.16 and 16.24 percentage points lower than the other two models.It is proved that the model has better prediction accuracy.It can be applied to the prediction of dry years,providing a theoretical basis for regional drought prediction and drought control.

关 键 词：灰色模型 干旱 SPI指数 平移转换 平均弱化缓冲算子 

分 类 号：P338[天文地球—水文科学]