UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 11 | Issue 11 | November 2024

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Published in:

Volume 11 Issue 10
October-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2410489


Registration ID:
549843

Page Number

e795-e804

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Title

Monitoring Of Arctic Precipitation Using AI/ML Techniques

Abstract

In the context of climate change and environmental shifts, monitoring arctic precipitation has emerged as a critical concern, particularly in under-researched regions where data scarcity hampers effective forecasting. This paper presents an innovative approach to enhancing precipitation prediction accuracy by employing advanced machine learning techniques, including Random Forest (RF), XGBoost, and Logistic Regression. The results reveal that Logistic Regression achieved the highest accuracy of 98%, while both Random Forest and XGBoost demonstrated an accuracy of 88%. By utilizing a comprehensive dataset containing key parameters such as date, cloud cover, sunshine, global radiation, temperature metrics, precipitation levels, and snow depth, our model aims to deliver timely and precise forecasts. The methodology integrates these machine learning algorithms to analyze and interpret the complex interactions among the meteorological factors, ultimately improving prediction outcomes. Our findings demonstrate that this integrated approach significantly enhances forecasting accuracy compared to traditional methods, making it a viable solution for practical applications in remote arctic regions. By facilitating early detection and understanding of precipitation patterns, this research contributes to better resource management and informed decision-making in response to the challenges posed by climate variability, ultimately aiming to mitigate the impacts of changing precipitation dynamics in vulnerable arctic ecosystems.

Key Words

Precipitation forecasting, Climate change, Machine learning, Random Forest (RF), XGBoost, Logistic Regression, Meteorological data analysis.

Cite This Article

"Monitoring Of Arctic Precipitation Using AI/ML Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e795-e804, October-2024, Available :http://www.jetir.org/papers/JETIR2410489.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Monitoring Of Arctic Precipitation Using AI/ML Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe795-e804, October-2024, Available at : http://www.jetir.org/papers/JETIR2410489.pdf

Publication Details

Published Paper ID: JETIR2410489
Registration ID: 549843
Published In: Volume 11 | Issue 10 | Year October-2024
DOI (Digital Object Identifier):
Page No: e795-e804
Country: MUMBAI SUBURBAN, Maharashtra, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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