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Doctorat
Inde
2017
New Statistical Approaches in Modeling and Forecasting Indian Rainfall Time Series
Titre : New Statistical Approaches in Modeling and Forecasting Indian Rainfall Time Series
Auteur : Kokila Ramesh
Université de soutenance : Jain University
Grade : Doctor of Philosophy (PhD) in Mathematics 2017
Résumé partiel
The annual rainfall of India has three seasons per year accounting for about 11% each
in the pre-monsoon (January-May) and the northeast monsoon (October-December)
and 78% in the southwest monsoon season also known as summer monsoon (JuneSeptember). The maximum amount of the rainfall occurs during southwest monsoon
(SWM), which governs the agricultural economy of India and hence for administrative
purposes. While the season recurs annually, the variation about the long term expected
value can be as high as 40-50% in some parts of the country. Variability during SWM
season is an uncertain quantity which India faces every year. This uncertainty can be
year to year, season to season (within year), month to month (within season and within
year) and so on depending on the requirement in the practical purposes. The huge
variation in the rainfall causes droughts and floods. The distress caused by droughts
and floods due to extreme variations of the monsoon can be mitigated to some extent
if the rainfall time series can be modeled efficiently for simulation and forecasting of
SWM data. Hence this becomes the primary reason to develop new models for Indian monsoon rainfall. Rainfall data is a strongly non-Gaussian time series exhibiting
non-stationarity.
The main objective of the present thesis is to develop new statistical approaches to
model and forecast Indian monsoon rainfall data. By considering only the rainfall and
its past relation, models have been constructed. These models are without the usage of
any atmospheric and oceanic related data parameters. The major areas touched upon in
this thesis include (a) Empirical mode decomposition analysis of Indian SWM rainfall
and seasonal data (three seasons per year data), (b) Modeling and Forecasting of Indian
monsoon rainfall using a new ANN model, (c) Inclusion of within year variation in the
ANN to model and forecast Indian SWM data with higher efficiency, (d) developing
a new non-Gaussian probability distribution model for the SWM data distribution of
core monsoon region and its subdivisions and (e) new non-Gaussian time series model
for simulation of Indian monsoon rainfall. The present thesis is structured into eight
chapters.
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