Quantile Regression Models for Rainfall Data - Damodharan, S; Venkatramana Reddy, S; Sarojamma, B (International Journal of Computer Sciences and Engineering, 2021)
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.