Machine Learning-Based Rainfall Prediction: A Systematic Methodology for Model Selection and Evaluation

This paper presents a comprehensive survey of contemporary techniques in rainfall prediction using time series forecasting within the framework of machine learning. It commences by underlining the pivotal role of rainfall prediction and its wide-ranging applications across various domains. A comprehensive review of diverse time series data types and their distinct characteristics is provided, followed by an exploration of data preprocessing and cleaning methods to facilitate accurate analysis. The paper extensively discusses the prevalent machine learning algorithms applied in rainfall prediction and delves into the efficacy of feature engineering techniques in enhancing predictive accuracy. The results show that Gradient Boosting achieved the highest accuracy of approximately 0.8924, making it the most accurate among the three models. Random Forest, with an accuracy of about 0.8639, performed well, but slightly less accurately than Gradient Boosting. Support Vector Machine (SVM) demonstrated the lowest accuracy of around 0.6017. The findings offer insights into the trade-offs between model accuracy and other considerations, such as interpretability and computational efficiency, helping guide the choice of the most suitable model for various applications.