Innovative Machine Learning Approaches for Prediction of Weather Parameters

Machine Learning (MD) has experienced significant growth in research in recent decades. Anticipated is a substantial impact of this technology on many facets of human existence. ML has revolutionized our interactions with the world, impacting industries ranging from healthcare to commerce, and from entertainment to transportation. Researchers are coming up with new algorithms and techniques to deal with complex issues, which means that ML is an essential tool in today’s technological landscape. As ML develops, its applications are projected to grow further leading to innovations and improved efficiency across various domains. This paper looks into recent and relevant scientific articles on ML approaches for weather variables analysis and numerical weather prediction. As meteorological data from several decades is recorded, we can predict wind speed, pressure, precipitation and temperature that are common studied meteorological parameters by utilizing ML techniques such as Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine etc. This is crucial for improving the accuracy and dependability of weather forecast. As it has been found in literature review, the diversity and complexity of meteorology data means that there is no any single ML technique which serves all parameters hence different methods should be used to make accurate analysis and predictions. This demonstrates the agility and flexibility of ML as it tackles various problems thus making it a key tool in modern scientific studies and technological development.