2026-09-18T22:17:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/293352022-01-13T07:54:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniele Veneziano.
Chou, Yi-Ju, 1976-
Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2005-10-14T19:55:30Z
2005-10-14T19:55:30Z
2003
2003
http://hdl.handle.net/1721.1/29335
52723462
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.
Includes bibliographical references (leaves 32-34).
This study develops a method to predict multifractal measure of temporal rainfall intensity by using Kalman filter, and gives some examples of prediction for generated rainfall. The model for the rainfall generation proposed here is established using a continuous-time, discrete-scale lognormal cascade (CLC) with AR(1) process for each component. This model allows us to simulate rainfall field with the property of the multifractality, which indicates the invariance for scaling of rainfall measure. Through the observation from the synthetic rainfall simulated by this model, Kalman filter is used as the tool for short-term rainfall prediction. We compare different results of predictions made under different simulations and discuss the extensions of this study, prediction for the wet/dry process while looking at real rainfall and issues about space-time rainfall modeling. Keywords: Multifractality, Bayesian estimation, Kalman filter.
by Yi-Ju Chou.
M.Eng.
53 leaves
1352801 bytes
1352609 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Short-term rainfall prediction using a multifractal model
Thesis
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<Abstract>This study develops a method to predict multifractal measure of temporal rainfall intensity by using Kalman filter, and gives some examples of prediction for generated rainfall. The model for the rainfall generation proposed here is established using a continuous-time, discrete-scale lognormal cascade (CLC) with AR(1) process for each component. This model allows us to simulate rainfall field with the property of the multifractality, which indicates the invariance for scaling of rainfall measure. Through the observation from the synthetic rainfall simulated by this model, Kalman filter is used as the tool for short-term rainfall prediction. We compare different results of predictions made under different simulations and discuss the extensions of this study, prediction for the wet/dry process while looking at real rainfall and issues about space-time rainfall modeling. Keywords: Multifractality, Bayesian estimation, Kalman filter.</Abstract>
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