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dc.contributorMulti-disciplinary Studiesen_US
dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorCheung, Tsz-wai-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/702-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titlePrediction models of rainfall in Hong Kongen_US
dcterms.abstractThe application and development of stochastic modeling techniques within the field of hydrology, such as the ARMA and transfer function models, has led to the establishment of stochastic hydrology. In this dissertation, a time series analysis on the monthly rainfall data from Hong Kong Observatory Headquarters is presented. The aim is to determine whether satisfactory models can be developed for real - time forecasting. The confirmation and the determination of the periodicity can be achieved by using spectral analysis. There are three different methods of modeling. After the appropriate models are obtained, two - year forecasts are then made using the Box - Jenkins difference equation. The first one was due to the Box and Jenkins (1994) approach of Seasonal ARMA model. The second method is the Exponential Smoothing Modeling. The pressure series and the monthly rainfall series are used to build up the model in the third method. Using pressure series as the input and rainfall series as the output develops the transfer function model. The regression strategy, also known as the linear transfer function modeling strategy, is applied in building of the model. The result shows that all models developed are far from satisfactory, only the Exponential Smoothing Model performs better in this dissertation.en_US
dcterms.extentviii, 122 leaves : ill. ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2001en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.LCSHRain and rainfall -- China -- Hong Kongen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/702