|Prediction models of rainfall in Hong Kong
|Hong Kong Polytechnic University -- Dissertations
Rain and rainfall -- China -- Hong Kong
Department of Applied Mathematics
|viii, 122 leaves : ill. ; 30 cm
|The 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.
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