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Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy
Men, Baohui1; Long, Rishang2; Li, Yangsong1; Liu, Huanlong1; Tian, Wei3,4; Wu, Zhijian1
2017-12-01
Source PublicationENTROPY
ISSN1099-4300
Volume19Issue:12Pages:15
Corresponding AuthorMen, Baohui(menbh@ncepu.edu.cn)
AbstractRainfall is an essential index to measure drought, and it is dependent upon various parameters including geographical environment, air temperature and pressure. The nonlinear nature of climatic variables leads to problems such as poor accuracy and instability in traditional forecasting methods. In this paper, the combined forecasting method based on data mining technology and cross entropy is proposed to forecast the rainfall with full consideration of the time-effectiveness of historical data. In view of the flaws of the fuzzy clustering method which is easy to fall into local optimal solution and low speed of operation, the ant colony algorithm is adopted to overcome these shortcomings and, as a result, refine the model. The method for determining weights is also improved by using the cross entropy. Besides, the forecast is conducted by analyzing the weighted average rainfall based on Thiessen polygon in the Beijing-Tianjin-Hebei region. Since the predictive errors are calculated, the results show that improved ant colony fuzzy clustering can effectively select historical data and enhance the accuracy of prediction so that the damage caused by extreme weather events like droughts and floods can be greatly lessened and even kept at bay.
Keywordrainfall forecast cross entropy ant colony fuzzy clustering combined forecast
DOI10.3390/e19120694
WOS KeywordARTIFICIAL NEURAL-NETWORK ; COMBINATION ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016YFC0401406] ; Famous Teachers Cultivation planning for Teaching of North China Electric Power University ; Education Reform Project of North China Electric Power University (Beijing Department)[2014JG57]
Funding OrganizationNational Key R&D Program of China ; Famous Teachers Cultivation planning for Teaching of North China Electric Power University ; Education Reform Project of North China Electric Power University (Beijing Department)
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000419007900063
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/60500
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMen, Baohui
Affiliation1.North China Elect Power Univ, Beijing Key Lab Energy Safety & Clean Utilizat, Renewable Energy Inst, Beijing 102206, Peoples R China
2.North China Elect Power Univ, State Key Lab New Energy Power Syst, Beijing 102206, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Men, Baohui,Long, Rishang,Li, Yangsong,et al. Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy[J]. ENTROPY,2017,19(12):15.
APA Men, Baohui,Long, Rishang,Li, Yangsong,Liu, Huanlong,Tian, Wei,&Wu, Zhijian.(2017).Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy.ENTROPY,19(12),15.
MLA Men, Baohui,et al."Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy".ENTROPY 19.12(2017):15.
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