An information-theoretic study of rainfall time series through the Dempster–Shafer approach over a meteorological subdivision of India

The present paper reports a study, where we have developed a methodology to understand the relative uncertainty associated with the rainfall amount corresponding to summer monsoon (JJAS) and post monsoon (OND) for the period 1871–2016 over northeast India. After calculating the partial correlation b...

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Bibliographic Details
Published in:Journal of hydroinformatics 2022-11, Vol.24 (6), p.1269-1280
Main Authors: Devi, Rashmi Rekha, Chattopadhyay, Surajit
Format: Article
Language:English
Subjects:
Adaptation
Climate change
dempster–shafer theory
fuzzy set
Fuzzy sets
Information theory
joint belief measure
Monsoons
northeast india
Precipitation
Prediction models
Probability
Rainfall
Random variables
Supervised learning
Time series
Trends
Uncertainty
Wind
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Summary:The present paper reports a study, where we have developed a methodology to understand the relative uncertainty associated with the rainfall amount corresponding to summer monsoon (JJAS) and post monsoon (OND) for the period 1871–2016 over northeast India. After calculating the partial correlation between two random variables after the removal of the effect of the third one, we have standardized all the realizations of the random variables. Subsequently, after applying the Dempster–Shafer theory, we have obtained joint basic assignments through two judging criteria for the fuzzy sets representing the closeness of the observed values to two measures of central tendency for different window sizes obtained from the original time series. The study revealed a higher rate of increase in the uncertainty with a change in the window size for OND than in the case of JJAS. This study finally concluded that this approach could generate some idea about the most advantageous ratio of training and test cases for predictive models with supervised learning procedures.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2022.192