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2019•Journal of Emerging Technologies and Innovative ResearchRequires access

Influential parameters on rainfall forecasting using multiple linear regression

N Shobha, T Asha

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Abstract

In this study, the function of Multiple linear regression is used to find the influential parameters on rainfall forecasting. The algorithm is applied on the dataset to determine importance of input parameters on the target variable. The input parameters include humidity, temperature, cloud amount, wind speed and surface pressure. The regression model was evaluated statistically with R2, adjusted R2, p-value and residual standard error values.

About this research paper

What this paper is about

In this study, the function of Multiple linear regression is used to find the influential parameters on rainfall forecasting. The algorithm is applied on the dataset to determine importance of input parameters on the target variable. The input parameters include humidity, temperature, cloud amount, wind speed and surface pressure. The regression model was evaluated statistically with R2, adjusted R2, p-value and residual standard error values.

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Method / approach

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Available abstract

In this study, the function of Multiple linear regression is used to find the influential parameters on rainfall forecasting. The algorithm is applied on the dataset to determine importance of input parameters on the target variable. The input parameters include humidity, temperature, cloud amount, wind speed and surface pressure. The regression model was evaluated statistically with R2, adjusted R2, p-value and residual standard error values.

Key concepts: Linear regression, Wind speed, Residual, Statistics, Regression analysis, Regression, Mathematics, Meteorology

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