RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2016, Vol. 25 >> Issue (08): 1273-1278.doi: 10.11870/cjlyzyyhj201608014

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STUDY ON DROUGHT CLASS PREDICTION METHOD BASED ON THE CLOUD REASONING AND FUZZY LOGIC RELATION MODEL

GONG Yan-bing1,2, DAI Liang-liang1, HU Na1, LIU Gao-feng1,2, ZHANG Ji-guo1,2   

  1. 1. Institute of Hydraulic Information Statistic and Management, Hohai University, Changzhou 213022, China;
    2. Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization, Nanjing 211100, China
  • Received:2015-11-25 Revised:2016-01-19 Online:2016-08-20
  • Supported by:
    National Nature Science Foundation of China (No. 71303074);Social Science Foundation of Jiangsu Province of China (No. 14GLC004);Natural Science Foundation of Jiangsu Province of China (No. BK20130242);Fundamental Research Fund for the Central University of China (Nos. 2015B23914, 2015B28014)

Abstract: For the monthly average precipitation time series having the features of the fuzziness and randomness, a drought class prediction combination method based on the cloud reasoning and fuzzy logic relation are given. By calculating the standardized precipitation index (SPI) of Xuzhou station monthly precipitation data from 1951 to 2014, we get the measured drought grade sequence. We use SPI data from1952 to 2013 as the sample data, and extract 52 fuzzy logic inference rules of the fuzzy time series. The SPI data of a month is used as the input variable, and the cloud generator is used to obtain the forecast result of the drought level in the future. The results show that this method has certain prediction ability for drought occurrence, especially for no drought prediction, which is more accurate. But it is weak for the drought state mutations prediction, mainly due to the possibility of a severe drought is less, resulting in a less fuzzy inference rules. Therefore, it can be used as a reference for early warning of drought in northern Jiangsu Province, which is given priority to the light drought.

Key words: SPI, cloud reasoning, fuzzy logic relation, drought class prediction

CLC Number: 

  • P338
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