Massaccesi, Luciano
(2024)
Interpolation of Annual Maximum rainfall probability distribution using Graph Neural Networks.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Artificial intelligence [LM-DM270]
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Abstract
The modelling of high resolution maximum rainfall depth distribution plays a
key role in preventing natural disasters, such as floods. The rain gauges in meteorological stations distributed over the territory already offer a good base to
estimate the maximum rainfall depth distribution. However, the resolution at
which the distribution is estimated can be increased through interpolation. In
this thesis, the problem of maximum rainfall depth distribution interpolation
is addressed by using a graph neural network-based approach. Some variations of the proposed algorithm are compared and evaluated on the northern
Italy dataset. In particular, the use of a combination of a simple ensemble and
temporal ensemble proved to be particularly effective. Finally, the proposed
methods show improved results when compared with the classical methods:
ordinary kriging, universal kriging and Gaussian process.
Abstract
The modelling of high resolution maximum rainfall depth distribution plays a
key role in preventing natural disasters, such as floods. The rain gauges in meteorological stations distributed over the territory already offer a good base to
estimate the maximum rainfall depth distribution. However, the resolution at
which the distribution is estimated can be increased through interpolation. In
this thesis, the problem of maximum rainfall depth distribution interpolation
is addressed by using a graph neural network-based approach. Some variations of the proposed algorithm are compared and evaluated on the northern
Italy dataset. In particular, the use of a combination of a simple ensemble and
temporal ensemble proved to be particularly effective. Finally, the proposed
methods show improved results when compared with the classical methods:
ordinary kriging, universal kriging and Gaussian process.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Massaccesi, Luciano
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
interpolation,graph neural network,maximum rainfall distribution,temporal ensemble,edge drop,jumping knowledge,eGAT
Data di discussione della Tesi
19 Marzo 2024
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Massaccesi, Luciano
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
interpolation,graph neural network,maximum rainfall distribution,temporal ensemble,edge drop,jumping knowledge,eGAT
Data di discussione della Tesi
19 Marzo 2024
URI
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