A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two models created: Model-1, which is based on daily Model-2, weekly data. A variety performance criteria to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, LGBM among the algorithms under consideration. This study provides insights into predictive abilities, revealing significant trends across phases. The results show that ensemble-based algorithms, particularly CatBoost outperform both emerged as model choice throughout all assessment stages, including testing. MAE was 0.00077, RMSE 0.0010, RMSPE 0.49, R2 0.99, confirming CatBoost’s unrivaled ability identify deep intricacies within patterns. Both had indicating remarkable predict trends. Significant XGBoost included 0.02 0.10, handle longer time intervals. Regression varies. Scatter plots demonstrate robustness demonstrating capacity sustain consistently low prediction errors dataset. emphasizes potential transform meteorology planning, improve decision-making through precise forecasts, contribute disaster preparedness measures.