Quantile Regression Models for Rainfall Data

Authors

  • Damodharan S Department of Statistics, S.V. University, Tirupati-517502, Andhra Pradesh, India
  • Venkatramana Reddy S Department of Statistics, S.V. University, Tirupati-517502, Andhra Pradesh, India
  • Sarojamma B Department of Statistics, S.V. University, Tirupati-517502, Andhra Pradesh, India

DOI:

https://doi.org/10.26438/ijcse/v9i9.8385

Keywords:

Rainfall, Quantile Regression,, Linear regression, RMSE

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.

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Published

2021-09-30
CITATION
DOI: 10.26438/ijcse/v9i9.8385
Published: 2021-09-30

How to Cite

[1]
S. Damodharan, S. Venkatramana Reddy, and B. Sarojamma, “Quantile Regression Models for Rainfall Data”, Int. J. Comp. Sci. Eng., vol. 9, no. 9, pp. 83–85, Sep. 2021.

Issue

Vol. 9 No. 9 (2021): IJCSE September Edition

Section

Research Article

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