MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day Article Swipe

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Ishu Gupta , Harsh Mittal , Deepak Rikhari , Ashutosh Kumar Singh ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2203.05835

Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and climatic changes using traditional meteorological techniques. With the advent of modern technologies and computing power, we can do so with the help of machine learning techniques. We aim to predict the weather of an area using past meteorological data and features using the Multiple Linear Regression Model. The performance of the model is evaluated and a conclusion is drawn. The model is successfully able to predict the average temperature of a day with an error of 2.8 degrees Celsius.

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Environmental Science

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Linear regression Weather prediction Regression Meteorology Regression analysis Weather forecasting Computer science Predictive power Environmental science Climatology Statistics Machine learning Mathematics Geography Geology Philosophy Epistemology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2203.05835
PDF: https://arxiv.org/pdf/2203.05835
OA Status: green
Cited By: 11
Related Works: 10
OpenAlex ID: https://openalex.org/W4221154964

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OpenAlex ID: https://openalex.org/W4221154964

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DOI: https://doi.org/10.48550/arxiv.2203.05835

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Title: MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-03-11

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Authors: Ishu Gupta, Harsh Mittal, Deepak Rikhari, Ashutosh Kumar Singh

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Landing page: https://arxiv.org/abs/2203.05835

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PDF URL: https://arxiv.org/pdf/2203.05835

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OA URL: https://arxiv.org/pdf/2203.05835

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Concepts: Linear regression, Weather prediction, Regression, Meteorology, Regression analysis, Weather forecasting, Computer science, Predictive power, Environmental science, Climatology, Statistics, Machine learning, Mathematics, Geography, Geology, Philosophy, Epistemology

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Cited by: 11

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Citations by year (recent): 2025: 1, 2024: 1, 2023: 5, 2022: 4

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Related works (count): 10

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