From Data to Forecast: A Comparative Evaluation of Machine Learning and Deep Learning Models for Rainfall Prediction in Australia

This study delves into the assessment of diverse machine learning and deep learning models in the context of rainfall prediction in Australia. Utilizing a comprehensive dataset encompassing all states in the country, sourced from the Australian Bureau of Meteorology, various models were evaluated. These encompassed Random Forest, Gradient Boosting, Logistic Regression, K-nearest Neighbors, Gaussian Naive Bayes, Decision Tree, XGBoost, as well as deep learning architectures like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). The evaluation was based on their respective test scores, unveiling significant disparities in predictive performance. Among the machine learning models, Gradient Boosting exhibited the highest performance with a test score of 0.9315, closely followed by Random Forest at 0.9282. Notably, these scores surpass those reported in existing models. In contrast, Logistic Regression displayed a comparatively lower performance at 0.7711. The deep learning models demonstrated varying degrees of accuracy, with Neural Network achieving a test score of 0.7954, while both RNN architectures, LSTM and GRU, displayed similar performance levels at 0.625. This study furnishes crucial insights into the effectiveness of different machine learning and deep learning models within this domain. It lays a foundation for future investigations, advocating for the exploration of more advanced techniques and feature engineering methodologies to further refine predictive capabilities in this field.