International Journal on Science and Technology

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Predict Weather with Machine Learning

Author(s) Tanuj jadon, Prince Pachauri, Aamir warsi
Country India
Abstract Everything, from farming to air travel, in our contemporary world is dependent on the weather forecast. However, traditional methods often ignore the intrinsic uncertainty in weather patterns leading to inaccurate predictions. This paper presents a groundbreaking method of predicting weather using Bayesian deep learning models; specifically, Bayesian neural networks are employed to capture the probabilistic nature of weather events and produce more accurate and reliable forecasts. These models represent a significant milestone in foretelling strategies as they are evaluated against popular metrics and trained with archived atmospheric data. Additionally, gradient boosting classifiers and decision tree classifiers have been performed as queries for performing comparative analyses about different machine learning algorithms used for predicting occurrence of rain tomorrow or any other type of climate-related information. For enhanced interpretability, this paper has developed interactive visualization tools that allow users to interactively explore predicted weather patterns and analyze levels of uncertainty with an aim of advancing the field of meteorology through providing actionable insights for responsible decision making within weather sensitive domains.
Keywords Weather forecasting, Bayesian deep learning models, Probabilistic aspect, Precise, Dependable, Interactive visualization tools, actionable insights.
Field Computer Applications
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-01-28
Cite This Predict Weather with Machine Learning - Tanuj jadon, Prince Pachauri, Aamir warsi - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.1561
DOI https://doi.org/10.71097/IJSAT.v16.i1.1561
Short DOI https://doi.org/g83jhg
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