Machine Learning Models for Long-Term Temperature and Precipitation Forecasting

Abstract- Climate prediction has always been a complex task owing to the nonlinear nature of climate systems. Conventional statistical models have limitations in capturing long-term dependencies, resulting in unreliable predictions. In this paper, we investigate the use of machine learning algorithms, namely neural networks, random forests, and support vector machines, for predicting long-term temperature and precipitation patterns. The models were trained and tested on past climate data to evaluate their predictability. The findings suggest that deep learning algorithms are more efficient than traditional methods, providing better accuracy in long-term climate prediction. This work will help in sustainable agricultural planning, water resource management, and disaster mitigation.