• Navjot Singh

Weather forecasting is a crucial application with significant impacts on various sectors, including agriculture, transportation, and disaster management. This review examines the utilization of ensemble learning techniques, specifically voting classifiers, in weather forecasting. Voting classifiers amalgamate predictions from multiple base learners to enhance forecasting accuracy and reliability. This paper synthesizes existing literature, discussing the implementation of diverse voting classifier variants, such as majority voting, weighted voting, and stacking, in weather prediction models. Additionally, it analyzes the advantages, challenges, and future prospects of employing ensemble methods for weather forecasting, offering insights into potential improvements and research directions.

Weather forecasting, ensemble learning, voting classifiers, ensemble methods, machine learning, weighted voting, stacking, accuracy, reliability

"Integrated Weather Prediction System: Enhancing Accuracy through Ensemble Learning and Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.f497-f501, December-2024, Available :http://www.jetir.org/papers/JETIR2412557.pdf

"Integrated Weather Prediction System: Enhancing Accuracy through Ensemble Learning and Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppf497-f501, December-2024, Available at : http://www.jetir.org/papers/JETIR2412557.pdf