Machine Learning Approaches for Climate Change Prediction: A Comparative Study

Abstract

This research explores various machine learning approaches, including deep learning and ensemble methods, to predict climate change indicators. We focus on temperature and precipitation trends using large datasets spanning multiple decades. By comparing the performance of algorithms like CNN, RNN, and random forests, we identify the most accurate models for specific climate variables. Our findings demonstrate that ensemble models provide better accuracy and reliability, especially for temperature predictions.