Accurate weather forecasting with dominant gradient boosting using machine learning

Suri babu Nuthalapati 1, * and Aravind Nuthalapati 2

1 Farmioc, Hyderabad, India.
2 Microsoft, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 408–422.
Article DOI: 10.30574/ijsra.2024.12.2.1246
Publication history: 
Received on 27 May 2024; revised on 04 July 2024; accepted on 07 July 2024
 
Abstract: 
This Paper examines the interesting topic of weather forecasting using ML. From kaggle.com, there is an extensive list of daily weather records for a Seattle dataset. In this chapter, gradient boosting outcomes are revealed as a result of careful data preparation and thorough examination of several machine learning models such as K-Nearest Neighbors, Support vector machine, Gradient Boosting, XGBOOST, logistic regression, and random forest class Its 80.95% accuracy was outstanding. ML traverses atmospheric dynamics that form a basis for weather predictions. It uses a highly developed method that enables it to predict the complex trends in the weather. In addition, machine learning algorithms are increasingly important for detecting non-linear relationships and patterns from large sets of complex data with time. It is critical for meteorologists in overcoming uncertainties associated with atmospheric dynamics to improve prediction. Gradient Boosting – a weather forecasting perspective in an interdisciplinary landscape involving weather science and machine learning. The current research on ML for weather forecasting has been very useful.
 
Keywords: 
Machine Learning; Weather Monitoring; Artificial Intelligence; XGBoost Classifiers
 
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