| Abstract: |
Climate change represents one of the most pressing global challenges, necessitating advanced computational approaches for accurate modeling and prediction. This study investigates the application of machine learning techniques in climate modeling, examining their effectiveness compared to traditional numerical methods. The primary objectives include evaluating deep learning architectures for temperature prediction, assessing ensemble methods for precipitation forecasting, and analyzing the computational efficiency of various algorithms. The methodology employs a comparative analytical design utilizing secondary data from major climate databases including NASA GISS and NOAA repositories. The hypothesis posits that machine learning models demonstrate superior predictive accuracy for short-term climate variables while maintaining computational efficiency. Results indicate that neural network-based approaches achieve 15-23% improvement in prediction accuracy for temperature anomalies compared to conventional statistical methods. Random forest and gradient boosting algorithms show particular promise for regional precipitation modeling with R² values exceeding 0.85. Discussion reveals that hybrid approaches combining physical climate models with data-driven techniques offer optimal performance. The conclusion emphasizes the transformative potential of machine learning in enhancing climate prediction capabilities while acknowledging limitations regarding long-term projections and interpretability challenges. |
