Eur. Phys. J. Plus (2023) 138: 719
https://doi.org/10.1140/epjp/s13360-023-04348-9

Technical Report

Probing the rainfall time series over northeast India through composite and binary fuzzy relation

Department of Mathematics, Amity University, Major Arterial Road, Action Area II, Rajarhat, New Town, 700135, Kolkata, India

^b surajitchatto@outlook.com

Received: 13 January 2023
Accepted: 3 August 2023
Published online: 16 August 2023

Abstract

The current work reports a binary fuzzy relation (BFR) based approach to insight into rainfall behavior over northeast India. In this work we have considered Pre monsoon ${(X}_1)$, Summer monsoon ${(X}_2)$, Post monsoon ${(X}_3)$ and Annual ${(X}_4)$ rainfall for the period 1871–2016 over Northeast India. We have considered BFR for the pairs ($X_1$, $X_2$), $(X_2,X_3),(X_3,X_4),(X_2,X_4)$. Composite BFRs with fuzzy cardinalities have proven that it is possible to understand the relative importance of seasonal rainfall in the annual rainfall over northeast India through fuzzy relations. We have concluded that the pre-monsoon rainfall has a moderate influence on the annual rainfall in northeast India. However, when composited with the summer monsoon, it influences the annual rainfall more significantly than when it is composited with post-monsoon rainfall. This study indicates that for developing a multivariate predictive model, it is possible to incorporate fuzziness in BFR to understand the relative importance of different predictors. Although the summer monsoon rainfall has the most significant role in the annual rainfall received by northeast India, the pre-monsoon rainfall also has some degree of influence powerful enough to develop a predictive model for annual rainfall over northeast India.