Literature Survey on Intelligent Rainfall Prediction Systems Using Advanced Machine Learning Algorithms

Rainfall prediction is crucial to efficient water resource management, agriculture, and catastrophe avoidance.Statistical models have gained popularity, yet machine learning approaches are progressively boosting prediction accuracy.SARIMA stands out for the ability to understand seasonal and time-based patterns within time series.This literature review offers SARIMA usage in rainfall prediction versus complex machine learning methods like LSTM, CNN-LSTM hybrids, and Random Forest, as reflected in recent literature.Results show that although SARIMA performs well for structured time series analysis, deep learning-based hybrid models can advance precision further through capturing complex spatial and sequential relationships.The research applies SARIMA using the Anaconda platform and Python packages like NumPy, Pandas, Matplotlib, Seaborn, and Stats models.The research is a comprehensive guide on how to improve SARIMA-based rainfall prediction by incorporating findings from hybrid machine learning methods.