Graphical model based spatio-temporal modeling of extreme events

A novel model is proposed in this thesis to describe in a flexible manner the extreme events in both spatial and temporal domain. This model can be used to model the occurrence of extreme events in different places and at different times. The model is based on the assumption that the block maxim...

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Main Author: Zhang, Liaofan
Other Authors: Justin Dauwels
Format: Final Year Project
Language:English
Published: 2013
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Online Access:http://hdl.handle.net/10356/54522
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-545222023-07-07T17:18:53Z Graphical model based spatio-temporal modeling of extreme events Zhang, Liaofan Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Mathematics and analysis A novel model is proposed in this thesis to describe in a flexible manner the extreme events in both spatial and temporal domain. This model can be used to model the occurrence of extreme events in different places and at different times. The model is based on the assumption that the block maximum (e.g. monthly or annually maximum) at each location and time instant follow a Generalized Extreme Value (GEV) distribution. The GEV parameters are then coupled together using a monoscale thin-membrane model across the space and a multiscale model across the time domain. Efficient inference algorithm has been proposed based on the framework of smoothing based optimization. In each loop of the optimization process, the GEV marginals are approximated by Gaussian unary potential function. The resulting problem can be simplified as a Gaussian graphical model inference problem and therefore embedded subgraph algorithm can be used to infer the marginal mean while low-rank approximation algorithm to learn the marginal variance. Synthetic and real data test are carried out to verify the accuracy and usefulness of the proposed model. Our results show that the importance of modeling extreme events in spatio-temporal domain, and demonstrate that the proposed model is a powerful tool for extreme events analysis as well. Bachelor of Engineering 2013-06-21T06:25:13Z 2013-06-21T06:25:13Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/54522 en Nanyang Technological University 85 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Mathematics and analysis
spellingShingle DRNTU::Engineering::Mathematics and analysis
Zhang, Liaofan
Graphical model based spatio-temporal modeling of extreme events
description A novel model is proposed in this thesis to describe in a flexible manner the extreme events in both spatial and temporal domain. This model can be used to model the occurrence of extreme events in different places and at different times. The model is based on the assumption that the block maximum (e.g. monthly or annually maximum) at each location and time instant follow a Generalized Extreme Value (GEV) distribution. The GEV parameters are then coupled together using a monoscale thin-membrane model across the space and a multiscale model across the time domain. Efficient inference algorithm has been proposed based on the framework of smoothing based optimization. In each loop of the optimization process, the GEV marginals are approximated by Gaussian unary potential function. The resulting problem can be simplified as a Gaussian graphical model inference problem and therefore embedded subgraph algorithm can be used to infer the marginal mean while low-rank approximation algorithm to learn the marginal variance. Synthetic and real data test are carried out to verify the accuracy and usefulness of the proposed model. Our results show that the importance of modeling extreme events in spatio-temporal domain, and demonstrate that the proposed model is a powerful tool for extreme events analysis as well.
author2 Justin Dauwels
author_facet Justin Dauwels
Zhang, Liaofan
format Final Year Project
author Zhang, Liaofan
author_sort Zhang, Liaofan
title Graphical model based spatio-temporal modeling of extreme events
title_short Graphical model based spatio-temporal modeling of extreme events
title_full Graphical model based spatio-temporal modeling of extreme events
title_fullStr Graphical model based spatio-temporal modeling of extreme events
title_full_unstemmed Graphical model based spatio-temporal modeling of extreme events
title_sort graphical model based spatio-temporal modeling of extreme events
publishDate 2013
url http://hdl.handle.net/10356/54522
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