Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/65551
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dc.titleEvidence of chaotic behavior in Singapore rainfall
dc.contributor.authorSivakumar, B.
dc.contributor.authorLiong, S.-Y.
dc.contributor.authorLiaw, C.-Y.
dc.date.accessioned2014-06-17T08:18:00Z
dc.date.available2014-06-17T08:18:00Z
dc.date.issued1998-04
dc.identifier.citationSivakumar, B.,Liong, S.-Y.,Liaw, C.-Y. (1998-04). Evidence of chaotic behavior in Singapore rainfall. Journal of the American Water Resources Association 34 (2) : 301-310. ScholarBank@NUS Repository.
dc.identifier.issn1093474X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/65551
dc.description.abstractThis paper focuses on the investigation of the existence of chaotic behavior in the Singapore rainfall data. The procedure for the determination of the minimum number of variables essential and the number of variables sufficient to model the dynamics of the rainfall process was studied. An analysis of the rainfall behavior of different time periods was also conducted. The correlation dimension was used as a basis for discriminating stochastic and chaotic behaviors. Daily rainfall records for durations of 30, 20, 10, 5, 4, 3, 2, and 1 years from six stations were analyzed. The delay time for the phase-space reconstruction was competed using the autocorrelation function approach. The results provide positive evidence of the existence of chaotic behavior in the daily rainfall data. The minimum number of variables essential to model the dynamics of the rainfall process was identified to be 3 while the number of variables sufficient to model the dynamics of the rainfall process ranges from 11 to 18. The results also suggest that the attractor dimensions of rainfall data of longer time periods are higher than that of shorter time periods. The study suggests a minimum number of 1500 data points required for the computation of the correlation dimension of the rainfall data.
dc.sourceScopus
dc.subjectChaotic and stochastic behaviors
dc.subjectCorrelation dimension
dc.subjectRainfall
dc.subjectShort-term prediction
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.description.sourcetitleJournal of the American Water Resources Association
dc.description.volume34
dc.description.issue2
dc.description.page301-310
dc.description.codenJWRAF
dc.identifier.isiutNOT_IN_WOS
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