Prediction of Seasonal and Annual Rainfall of Pune and Mahabaleshwar using Multiple Linear Regression Models

Vivekanandan N.¹

1. Central Water and Power Research Station, Pune 411024, Maharashtra, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.3 , pp.35-41, Mar-2023

Online published on Mar 31, 2023

Copyright © Vivekanandan N. . 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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Abstract :
Prediction of seasonal and annual rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support. This paper aims to predict the seasonal (monsoon and post-monsoon) and annual rainfall for Pune and Mahabaleshwar through multiple linear regression (MLR) models viz., Regression Model-1 (RM1), Regression Model-2 (RM2) and Regression Model-3 (RM3). The meteorological data such as rainfall (R), minimum temperature (Tmin), maximum temperature (Tmax), average wind speed (AWS) and relative humidity (RH) is used. The seasonal and annual series of meteorological data is extracted from the daily data and used for prediction of rainfall through three regression models, which are evaluated by correlation coefficient (CC), Nash-Sutcliffe model efficiency (NSE) and root mean squared error (RMSE). The study shows the RMSE on predicted seasonal and annual rainfall using RM3 (with R, Tmin, Tmax, AWS and RH) model is minimum than those values of RM1 (with R, Tmin and Tmax) and RM2 (with R, Tmin, Tmax and AWS) models for Pune and Mahabaleshwar. The study also shows the NSE in rainfall prediction using RM3 is higher than those values given by RM1 and RM2. The CC values in seasonal and annual rainfall prediction using RM1, RM2 and RM3 vary from 0.906 to 0.973 for Pune while 0.963 to 0.987 for Mahabaleshwar. The paper presents the RM3 is better suited regression model for prediction of seasonal and annual rainfall for Pune and Mahabaleshwar.

Key-Words / Index Term :
Correlation coefficient, Mean squared error, Model efficiency, Rainfall, Regression

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