Utilising machine learning for improved weather forecasting and analysis

Author(s)	Sunny kumar, Prince raj, Chandan kumar
Country	India
Abstract	Weather forecasting has long been an essential application in various domains, including agriculture, transportation, disaster management, and daily planning. Traditional methods based on physical and statistical models have limitations in accuracy and computational efficiency. With advancements in Artificial Intelligence (AI) and Machine Learning (ML), these technologies have emerged as powerful tools for enhancing weather prediction. This paper explores how AI and ML are transforming weather forecasting, discussing key methodologies, models, datasets, and challenges while providing an overview of their current and potential applications.
Keywords	data analysis, satellite imagery, historical weather data, real-time sensor data, deep learning, neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), machine learning algorithms, weather patterns, precipitation prediction, extreme weather events, data preprocessing, model training, ensemble forecasting, probabilistic forecasting, climate change, accuracy improvement, localized forecasting, IoT integration, and explainable AI
Field	Computer Applications
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-02-02
Cite This	Utilising machine learning for improved weather forecasting and analysis - Sunny kumar, Prince raj, Chandan kumar - IJSAT Volume 16, Issue 1, January-March 2025.

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