EventsMOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Event submissions
Published
with-doi10.3390/mol2net-08-12789
This submission belongs to the session 05. NICE.XSM-08: North-Ibero-America Congress on Exp. & Simul. Methods, Valencia, Spain-Miami, USA, 2022 of the event MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Published date
09 Jul, 2022
Academic Editor
author-avatarSonia Arrasate Gil
Citation
Sweta Chakraborty, Sarbari Ghosh, Subrata Kumar Midya, Quantitative Prediction of Pre-Monsoon Rainfall in a Metro City of India, in Proceedings of MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed., 1 January–15 January 2023, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-08-12789
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Quantitative Prediction of Pre-Monsoon Rainfall in a Metro City of India

Sweta Chakraborty 1
Sarbari Ghosh 2,3
Subrata Kumar Midya 4
1. Research Scholar, Department of Atmospheric Sciences, University of Calcutta, Kolkata,India
2. Associate Professor , Department of Mathematics, Vidyasagar Metropolitan College,Kolkata,India
3. Guest Faculty, Department of Atmospheric Sciences, University of Calcutta,Kolkata,India
4. Professor, Department of Atmospheric Sciences University of Calcutta,Kolkata,India
Abstract

The present study mainly aims at the quantitative prediction of rainfall during the pre-monsoon season at Kolkata (22° 34' N, 88° 24' E), India, for the next 24 hours from the time of observation. For this purpose multiple linear regression models are developed separately for March, April, May and the Whole Pre-Monsoon Season. The total period of the study is 1974-2014 (41 years) among which data set (1974-2001) is used to select the important parameters for constructing the models and that of (2002-2014) is used for validation. The parameters are selected on the basis of stepwise linear regression, backward selection procedure and ANOVA.

It is interesting to note that the multiple linear regression models thus developed are capable of predicting the moderate range rainfall (7.5mm-35.55mm) almost accurately with the maximum error lying between ±10mm.

Though the residual plots indicate that there are no such obvious defects present in the models, the RMSE values reveal that the models for May and Whole Pre-Monsoon Season produce better results than the others.

Keywords
Pre-monsoon rainfall
Multiple Linear Regression (MLR)
Residual Plot
Root mean square error (RMSE)
Analysis of variance (ANOVA)
Manuscript
Manuscript.pdf
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