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Quantile Regression Models for Rainfall Data

S. Damodharan1 , S. Venkatramana Reddy2 , B. Sarojamma3

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-9 , Page no. 83-85, Sep-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i9.8385

Online published on Sep 30, 2021

Copyright © S. Damodharan, S. Venkatramana Reddy, B. Sarojamma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Damodharan, S. Venkatramana Reddy, B. Sarojamma, “Quantile Regression Models for Rainfall Data,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.9, pp.83-85, 2021.

MLA Style Citation: S. Damodharan, S. Venkatramana Reddy, B. Sarojamma "Quantile Regression Models for Rainfall Data." International Journal of Computer Sciences and Engineering 9.9 (2021): 83-85.

APA Style Citation: S. Damodharan, S. Venkatramana Reddy, B. Sarojamma, (2021). Quantile Regression Models for Rainfall Data. International Journal of Computer Sciences and Engineering, 9(9), 83-85.

BibTex Style Citation:
@article{Damodharan_2021,
author = {S. Damodharan, S. Venkatramana Reddy, B. Sarojamma},
title = {Quantile Regression Models for Rainfall Data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2021},
volume = {9},
Issue = {9},
month = {9},
year = {2021},
issn = {2347-2693},
pages = {83-85},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5403},
doi = {https://doi.org/10.26438/ijcse/v9i9.8385}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i9.8385}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5403
TI - Quantile Regression Models for Rainfall Data
T2 - International Journal of Computer Sciences and Engineering
AU - S. Damodharan, S. Venkatramana Reddy, B. Sarojamma
PY - 2021
DA - 2021/09/30
PB - IJCSE, Indore, INDIA
SP - 83-85
IS - 9
VL - 9
SN - 2347-2693
ER -

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Abstract

Rainfall is important for human beings, animals and plants for their survival. Rainfall depends on many variables such as wind speed, temperature, humidity etc. Mathematical modelling of rainfall data is a stochastic process. Several mathematical models based on the probability concept are available. These models help in knowing the probable weekly, monthly or annually rainfall. Over the past decade or so, a number of models have been developed to generate rainfall and runoff. Monthly rainfall and temperature were analyzed using time series analysis. In this paper we are fitted linear regression model and quartile regression model at various values of tau 0.25, 0.5 and 0.75 for North west India (NWI), West Central India (WCI), North East India(NEI), Central North East India (CNEI) and Peninsular India (PI). Best model among fitted four models is choosing by using root mean square error (RMSE) criteria.

Key-Words / Index Term

Rainfall, Quantile Regression, Linear regression, RMSE

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