The Role of Machine Learning in Climate Modeling

Authors

  • Sam Hall PhD
  • Kim Brown PhD
  • Casey Edwards PhD

Keywords:

machine learning, climate modeling, predictive algorithms, climate change, data integration

Abstract

This paper examines the transformative role of machine learning in climate modeling, providing tools that enhance the accuracy of climate predictions. By integrating vast datasets and advanced algorithms, machine learning models enable researchers to simulate complex climate systems more effectively. The study highlights significant improvements in predictive capabilities, offering new insights into climate change patterns and potential mitigation strategies.
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Author Biographies

Sam Hall, PhD

PhD
University of São Paulo
R. da Reitoria, 374 - Butantã, São Paulo - SP, 05508-220, Brazil

Kim Brown, PhD

PhD
Indian Institute of Science
CV Raman Rd, Bengaluru, Karnataka 560012, India

Casey Edwards, PhD

PhD
University of Cape Town
Rondebosch, Cape Town, 7700, South Africa

References

Климов, Н. Д. (2024). ПРИМЕНЕНИЕ АВТОМАТИЗАЦИИ В РЕГРЕССИОННОМ ТЕСТИРОВАНИИ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ. apni. ru Редакционная коллегия, 8.

KUMAR, Nitin; KATARIA, Vipin. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.

Рагимов, Э. (2011). КВАЛИМЕТРИЧЕСКИЕ ПОКАЗАТЕЛИ БЕЗОПАСНОСТИ ПРОГРАММНЫХ КОМПЛЕКСОВ, РЕАЛИЗУЮЩИХ СИСТЕМУ УПРАВЛЕНИЯ КОРПОРАТИВНЫМИ СЕТЯМИ. Problems of information technology, 2(1), 18-23.

Published

2024-12-25

Issue

Section

Articles