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dc.contributor.authorChen, Lien_US
dc.contributor.authorYeh, Keh-Chiaen_US
dc.contributor.authorWei, Hsiao-Pingen_US
dc.contributor.authorLiu, Gin-Rongen_US
dc.date.accessioned2014-12-08T15:29:43Z-
dc.date.available2014-12-08T15:29:43Z-
dc.date.issued2011-07-30en_US
dc.identifier.issn0885-6087en_US
dc.identifier.urihttp://dx.doi.org/10.1002/hyp.8132en_US
dc.identifier.urihttp://hdl.handle.net/11536/21349-
dc.description.abstractThis article proposes an improved multi-run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi-variable meteorological satellite data. GP is a well-known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi-variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back-propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright. (C) 2011 John Wiley & Sons, Ltd.en_US
dc.language.isoen_USen_US
dc.subjectgenetic programmingen_US
dc.subjectmeteorological satelliteen_US
dc.subjectSSM/Ien_US
dc.subjectevolutionary programmingen_US
dc.subjectdata miningen_US
dc.subjectback-propagated network (BPN)en_US
dc.titleAn improved genetic programming to SSM/I estimation typhoon precipitation over oceanen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/hyp.8132en_US
dc.identifier.journalHYDROLOGICAL PROCESSESen_US
dc.citation.volume25en_US
dc.citation.issue16en_US
dc.citation.spage2573en_US
dc.citation.epage2583en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000294200800009-
dc.citation.woscount4-
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