PREDICTION OF RAINFALL WITH A LINEAR REGRESSION FOR MULTIPLE WEATHER DATA-VARIABLES BY INCORPORATING THE WEIGHTED MOVING AVERAGE FILTER

ICTACT Journal on Data Science and Machine Learning ( Volume: 4 , Issue: 2 )

Abstract

Factors like traffic volume, the make and model of the vehicles, and driver behaviour are just as crucial to a road’s operational performance and safety as the weather. Weather conditions like fog and rain have an impact on visibility, which in turn affects how frequently accidents happen on a given road or the likelihood of getting into an accident. In this study, meteorological data were analysed using multiple linear regression to forecast precipitation and visibility for the benefit of various stockholders. For the purpose of analysing the data (10 years with 4018 samples), the mean square error (MSE) and its rooted version (RMSE), mean absolute error (MAE), and R-squared are used. As a result, the only way to judge the precision of models is through residuals. The empirical findings can be used by practitioners. The findings that were discovered to produce forecasts for the visibility and the rainfall.

Authors

Ruhiat Sultana, Mehveen Mehdi Khatoon, Muneeba Zuha
Bhoj Reddy Engineering College for Women, India

Keywords

Linear Regression, Prediction Accuracy, Weather Data, Correlation

Published By
ICTACT
Published In
ICTACT Journal on Data Science and Machine Learning
( Volume: 4 , Issue: 2 )
Date of Publication
March 2023
Pages
420 - 424
DOI

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