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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM31952

Weather Forecasting: A Comprehensive Exploration of IoT and Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM31952

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM31952

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

NM

Nayana More

TS

Tejal Salve

SP

Swarna Patil

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Abstract

Weather forecasting play an important role in human lives.It is impacting on various human activities such as agriculture and transportation.Changes in climate conditions often lead to unpredictable weather. Conventional forecasting models sometimes provide inaccurate predictions because they don’t adapt well to the dynamic nature of climate.The integration of (IoT) and Machine Learning (ML) technologies has revolutionized weather forecasting methodologies.This paper explores how combining Machine Learning (ML) algorithms with Internet of Things (IoT) devices improves weather forecasting accuracy and efficiency. By analyzing real-time data and historical patterns, this paper shows how IoT and ML can offer more accurate and timely forecasts compare to the limitations of traditional methods.Accurate weather forecasts are really important for things like farming, transportation, saving energy.In this paper the effectiveness of the algorithm is evaluated using various performance metrics including r2 score comparison,mean square error comparison.

KEYWORDS

IOT Machine Learning Support Vector Machine

Cite This Article

Nayana More, Tejal Salve, Swarna Patil, "Weather Forecasting: A Comprehensive Exploration of IoT and Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM31952

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12/31/45415

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