Abstract
When considering the operational rendition and safety of a road, factors such as traffic condition, vehicle characteristics, and driver behaviour are just as important as the weather condition. In particular, the visibility and, by extension, the frequency with which accidents occur on a certain route or the probability of getting involved in accidents are affected by weather conditions like fog and rain. In this paper, a multiple linear regression based analysis of weather data is performed for predicting the precipitation and visibility so that different stockholder can be facilitated. This analysis method looks at the relationship between several independent variables (such as temperature, humidity, cloud-cover) and a dependent variable (precipitation or visibility). To evaluate the accuracy of the regression models, several evaluation metrics, including the mean square error (MSE), root mean square error (RMSE), and the mean absolute error (MAE), as well as the R-squared value have been employed on a large dataset containing 10 years of weather data, with 4018 samples. The obtained results proves that the multiple linear regression models can provide more accurate predictions of future weather conditions, benefiting a wide range of industries and individuals who depend on it for their operations and decision making.
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Singh, G., Harun (2023). Multiple Linear Regression Based Analysis of Weather Data for Precipitation and Visibility Prediction. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_6
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